针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.616 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6166666666666667
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.748 (+/-0.449) for {'C': 1.0, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.639 (+/-0.326) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.615 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6663461538461538
RBF的粗grid search最佳超参: {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0} RBF的粗grid search最高分值: 0.6166666666666667
linear的粗grid search最佳超参: {'C': 1.0, 'kernel': 'linear'} linear的粗grid search最高分值: 0.6663461538461538
粗grid search得到的parameter是:
[{'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['rbf'], 'gamma': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.689 (+/-0.436) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.664 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.664 (+/-0.388) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.662 (+/-0.389) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'linear'}
0.714 (+/-0.417) for {'C': 10.0, 'kernel': 'linear'}
0.639 (+/-0.326) for {'C': 100.0, 'kernel': 'linear'}
0.639 (+/-0.326) for {'C': 1000.0, 'kernel': 'linear'}
0.639 (+/-0.326) for {'C': 10000.0, 'kernel': 'linear'}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [1]
检查交叉验证集中测试集标签是否有预测不到的值: {-1}
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 1.00 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.640 (+/-0.324) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.616 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.640 (+/-0.324) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.616 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.640 (+/-0.323) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'linear'}
0.665 (+/-0.314) for {'C': 10.0, 'kernel': 'linear'}
0.615 (+/-0.238) for {'C': 100.0, 'kernel': 'linear'}
0.615 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
0.615 (+/-0.238) for {'C': 10000.0, 'kernel': 'linear'}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 10.0, 'kernel': 'linear'}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.665224358974359
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.672 (+/-0.392) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.689 (+/-0.374) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.689 (+/-0.436) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.383) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.689 (+/-0.382) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.664 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.689 (+/-0.382) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.664 (+/-0.388) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.681 (+/-0.372) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.662 (+/-0.389) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 1.0, 'kernel': 'linear'}
0.681 (+/-0.372) for {'C': 10.0, 'kernel': 'linear'}
0.639 (+/-0.326) for {'C': 100.0, 'kernel': 'linear'}
0.639 (+/-0.326) for {'C': 1000.0, 'kernel': 'linear'}
0.639 (+/-0.326) for {'C': 10000.0, 'kernel': 'linear'}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.641 (+/-0.235) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.315) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.666 (+/-0.315) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.640 (+/-0.324) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.315) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.640 (+/-0.324) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.666 (+/-0.314) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.640 (+/-0.323) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.664 (+/-0.315) for {'C': 10.0, 'kernel': 'linear'}
0.615 (+/-0.238) for {'C': 100.0, 'kernel': 'linear'}
0.615 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
0.615 (+/-0.238) for {'C': 10000.0, 'kernel': 'linear'}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6663461538461538
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.689 (+/-0.436) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.664 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.664 (+/-0.388) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.662 (+/-0.389) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.689 (+/-0.436) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.681 (+/-0.372) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.714 (+/-0.417) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [1]
检查交叉验证集中测试集标签是否有预测不到的值: {-1}
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 1.00 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.640 (+/-0.324) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.616 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.640 (+/-0.324) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.616 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.640 (+/-0.323) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.641 (+/-0.235) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.665 (+/-0.314) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 9.999999999999998, 'kernel': 'linear'}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.665224358974359
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.672 (+/-0.392) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.689 (+/-0.374) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.689 (+/-0.436) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.383) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.689 (+/-0.382) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.664 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.689 (+/-0.382) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.664 (+/-0.388) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.681 (+/-0.372) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.662 (+/-0.389) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.689 (+/-0.382) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.659 (+/-0.313) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.663 (+/-0.372) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.635 (+/-0.307) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.681 (+/-0.372) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.641 (+/-0.235) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.315) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.666 (+/-0.315) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.640 (+/-0.324) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.315) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.640 (+/-0.324) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.666 (+/-0.314) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.640 (+/-0.323) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.666 (+/-0.315) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.665 (+/-0.313) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.664 (+/-0.312) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.663 (+/-0.313) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.664 (+/-0.315) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 0.6309573444801931, 'kernel': 'linear'}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6663461538461538
循环迭代之前,delta is: [0.36904266]
执行tunemodel()函数前,使用的grid search parameter是:
[{'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['rbf'], 'gamma': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05])}, {'C': array([0.39810717, 0.43651583, 0.47863009, 0.52480746, 0.57543994,
0.63095734, 0.69183097, 0.75857758, 0.83176377, 0.91201084,
1. ]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.689 (+/-0.436) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.664 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.664 (+/-0.388) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.662 (+/-0.389) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.43651583224016594, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.47863009232263826, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.5248074602497725, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.5754399373371568, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.6918309709189364, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.7585775750291837, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.8317637711026709, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.9120108393559095, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.9999999999999997, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [1]
检查交叉验证集中测试集标签是否有预测不到的值: {-1}
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 1.00 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.640 (+/-0.324) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.616 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.640 (+/-0.324) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.616 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.640 (+/-0.323) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.43651583224016594, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.47863009232263826, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.5248074602497725, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.5754399373371568, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.6918309709189364, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.7585775750291837, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.8317637711026709, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.9120108393559095, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.9999999999999997, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6397435897435898
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.672 (+/-0.392) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.689 (+/-0.374) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.689 (+/-0.436) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.383) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.689 (+/-0.382) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.664 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.689 (+/-0.382) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.664 (+/-0.388) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.681 (+/-0.372) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.662 (+/-0.389) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.43651583224016594, 'kernel': 'linear'}
0.797 (+/-0.492) for {'C': 0.47863009232263826, 'kernel': 'linear'}
0.772 (+/-0.473) for {'C': 0.5248074602497725, 'kernel': 'linear'}
0.747 (+/-0.449) for {'C': 0.5754399373371568, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.6918309709189364, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.7585775750291837, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.8317637711026709, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.9120108393559095, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.9999999999999997, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.641 (+/-0.235) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.315) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.666 (+/-0.315) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.640 (+/-0.324) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.315) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.640 (+/-0.324) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.666 (+/-0.314) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.640 (+/-0.323) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.43651583224016594, 'kernel': 'linear'}
0.642 (+/-0.236) for {'C': 0.47863009232263826, 'kernel': 'linear'}
0.642 (+/-0.236) for {'C': 0.5248074602497725, 'kernel': 'linear'}
0.641 (+/-0.236) for {'C': 0.5754399373371568, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.6918309709189364, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.7585775750291837, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.8317637711026709, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.9120108393559095, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.9999999999999997, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 0.6309573444801931, 'kernel': 'linear'}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6663461538461538
第0轮loop中,tunemodel函数得到的最优参数是: ([{'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['rbf'], 'gamma': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05])}, {'C': array([0.57543994, 0.58613816, 0.59703529, 0.608135 , 0.61944108,
0.63095734, 0.64268772, 0.65463617, 0.66680677, 0.67920363,
0.69183097]), 'kernel': ['linear']}], {'C': 0.6309573444801931, 'kernel': 'linear'}, 0.6663461538461538)
这是第0次迭代微调C和gamma。
第0次迭代,得到delta: [0.]
SVC模型clf_op_iter的参数是: {'kernel': 'linear', 'decision_function_shape': 'ovr', 'class_weight': {-1: 30}, 'tol': 0.001, 'gamma': 'auto', 'random_state': 0, 'cache_size': 200, 'verbose': False, 'degree': 3, 'C': 0.6309573444801931, 'probability': False, 'shrinking': True, 'coef0': 0.0, 'max_iter': -1}
训练模型SVC对训练样本的预测准确率: 0.9029187817258884
测试集中,预测为舞弊样本的有: (array([ 370, 658, 1246, 1247, 1248, 1251, 1252, 1255, 1256], dtype=int64),)
测试集中,实际为舞弊样本的有: (array([1246, 1247, 1248, 1249, 1250, 1251, 1252, 1253, 1254, 1255, 1256],
dtype=int64),)
预测的舞弊样本数目: 9
训练模型SVC对测试样本的预测准确率: 0.9225888324873096
以上是第0次特征筛选。
第0次特征筛选,AUC值是: 0.8173792499635195
X_train_iter_svc.shape is: (1257, 52)
X_test_iter_svc.shape is: (1257, 52)
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.616 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6166666666666667
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.748 (+/-0.449) for {'C': 1.0, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.639 (+/-0.326) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.615 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6663461538461538
RBF的粗grid search最佳超参: {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0} RBF的粗grid search最高分值: 0.6166666666666667
linear的粗grid search最佳超参: {'C': 1.0, 'kernel': 'linear'} linear的粗grid search最高分值: 0.6663461538461538
粗grid search得到的parameter是:
[{'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['rbf'], 'gamma': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.689 (+/-0.436) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.664 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.664 (+/-0.388) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'linear'}
0.714 (+/-0.417) for {'C': 10.0, 'kernel': 'linear'}
0.639 (+/-0.326) for {'C': 100.0, 'kernel': 'linear'}
0.639 (+/-0.326) for {'C': 1000.0, 'kernel': 'linear'}
0.639 (+/-0.326) for {'C': 10000.0, 'kernel': 'linear'}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [1]
检查交叉验证集中测试集标签是否有预测不到的值: {-1}
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 1.00 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.640 (+/-0.324) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.616 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.640 (+/-0.324) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.616 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.640 (+/-0.324) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'linear'}
0.665 (+/-0.314) for {'C': 10.0, 'kernel': 'linear'}
0.615 (+/-0.238) for {'C': 100.0, 'kernel': 'linear'}
0.615 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
0.615 (+/-0.238) for {'C': 10000.0, 'kernel': 'linear'}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 10.0, 'kernel': 'linear'}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.665224358974359
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.672 (+/-0.392) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.689 (+/-0.374) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.689 (+/-0.436) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.383) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.689 (+/-0.382) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.664 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.689 (+/-0.382) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.664 (+/-0.388) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.664 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 1.0, 'kernel': 'linear'}
0.681 (+/-0.372) for {'C': 10.0, 'kernel': 'linear'}
0.639 (+/-0.326) for {'C': 100.0, 'kernel': 'linear'}
0.639 (+/-0.326) for {'C': 1000.0, 'kernel': 'linear'}
0.639 (+/-0.326) for {'C': 10000.0, 'kernel': 'linear'}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.641 (+/-0.235) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.315) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.666 (+/-0.315) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.640 (+/-0.324) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.315) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.640 (+/-0.324) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.666 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.640 (+/-0.324) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.664 (+/-0.315) for {'C': 10.0, 'kernel': 'linear'}
0.615 (+/-0.238) for {'C': 100.0, 'kernel': 'linear'}
0.615 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
0.615 (+/-0.238) for {'C': 10000.0, 'kernel': 'linear'}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6663461538461538
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.689 (+/-0.436) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.664 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.664 (+/-0.388) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.689 (+/-0.436) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.714 (+/-0.418) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.714 (+/-0.417) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [1]
检查交叉验证集中测试集标签是否有预测不到的值: {-1}
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 1.00 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.640 (+/-0.324) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.616 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.640 (+/-0.324) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.616 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.640 (+/-0.324) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.641 (+/-0.235) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.665 (+/-0.314) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 9.999999999999998, 'kernel': 'linear'}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.665224358974359
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.672 (+/-0.392) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.689 (+/-0.374) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.689 (+/-0.436) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.383) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.689 (+/-0.382) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.664 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.689 (+/-0.382) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.664 (+/-0.388) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.664 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.689 (+/-0.382) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.659 (+/-0.313) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.663 (+/-0.372) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.671 (+/-0.376) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.681 (+/-0.372) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.641 (+/-0.235) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.315) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.666 (+/-0.315) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.640 (+/-0.324) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.315) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.640 (+/-0.324) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.666 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.640 (+/-0.324) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.666 (+/-0.315) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.665 (+/-0.313) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.664 (+/-0.312) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.663 (+/-0.314) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.664 (+/-0.315) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 0.6309573444801931, 'kernel': 'linear'}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6663461538461538
循环迭代之前,delta is: [0.36904266]
执行tunemodel()函数前,使用的grid search parameter是:
[{'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['rbf'], 'gamma': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05])}, {'C': array([0.39810717, 0.43651583, 0.47863009, 0.52480746, 0.57543994,
0.63095734, 0.69183097, 0.75857758, 0.83176377, 0.91201084,
1. ]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.689 (+/-0.436) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.664 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.664 (+/-0.388) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.43651583224016594, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.47863009232263826, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.5248074602497725, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.5754399373371568, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.6918309709189364, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.7585775750291837, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.8317637711026709, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.9120108393559095, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.9999999999999997, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [1]
检查交叉验证集中测试集标签是否有预测不到的值: {-1}
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 1.00 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.640 (+/-0.324) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.616 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.640 (+/-0.324) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.616 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.640 (+/-0.324) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.43651583224016594, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.47863009232263826, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.5248074602497725, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.5754399373371568, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.6918309709189364, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.7585775750291837, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.8317637711026709, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.9120108393559095, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.9999999999999997, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6397435897435898
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.672 (+/-0.392) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.689 (+/-0.374) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.689 (+/-0.436) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.383) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.689 (+/-0.382) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.664 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.689 (+/-0.382) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.664 (+/-0.388) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.664 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.43651583224016594, 'kernel': 'linear'}
0.797 (+/-0.492) for {'C': 0.47863009232263826, 'kernel': 'linear'}
0.772 (+/-0.473) for {'C': 0.5248074602497725, 'kernel': 'linear'}
0.747 (+/-0.449) for {'C': 0.5754399373371568, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.6918309709189364, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.7585775750291837, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.8317637711026709, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.9120108393559095, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.9999999999999997, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.641 (+/-0.235) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.315) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.666 (+/-0.315) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.640 (+/-0.324) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.315) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.640 (+/-0.324) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.666 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.640 (+/-0.324) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.43651583224016594, 'kernel': 'linear'}
0.642 (+/-0.236) for {'C': 0.47863009232263826, 'kernel': 'linear'}
0.642 (+/-0.236) for {'C': 0.5248074602497725, 'kernel': 'linear'}
0.641 (+/-0.236) for {'C': 0.5754399373371568, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.6918309709189364, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.7585775750291837, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.8317637711026709, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.9120108393559095, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.9999999999999997, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 0.6309573444801931, 'kernel': 'linear'}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6663461538461538
第0轮loop中,tunemodel函数得到的最优参数是: ([{'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['rbf'], 'gamma': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05])}, {'C': array([0.57543994, 0.58613816, 0.59703529, 0.608135 , 0.61944108,
0.63095734, 0.64268772, 0.65463617, 0.66680677, 0.67920363,
0.69183097]), 'kernel': ['linear']}], {'C': 0.6309573444801931, 'kernel': 'linear'}, 0.6663461538461538)
这是第0次迭代微调C和gamma。
第0次迭代,得到delta: [0.]
SVC模型clf_op_iter的参数是: {'kernel': 'linear', 'decision_function_shape': 'ovr', 'class_weight': {-1: 30}, 'tol': 0.001, 'gamma': 'auto', 'random_state': 0, 'cache_size': 200, 'verbose': False, 'degree': 3, 'C': 0.6309573444801931, 'probability': False, 'shrinking': True, 'coef0': 0.0, 'max_iter': -1}
训练模型SVC对训练样本的预测准确率: 0.9029187817258884
测试集中,预测为舞弊样本的有: (array([ 370, 658, 1246, 1247, 1248, 1251, 1252, 1255, 1256], dtype=int64),)
测试集中,实际为舞弊样本的有: (array([1246, 1247, 1248, 1249, 1250, 1251, 1252, 1253, 1254, 1255, 1256],
dtype=int64),)
预测的舞弊样本数目: 9
训练模型SVC对测试样本的预测准确率: 0.9225888324873096
以上是第1次特征筛选。
第1次特征筛选,AUC值是: 0.8173792499635195
X_train_iter_svc.shape is: (1257, 51)
X_test_iter_svc.shape is: (1257, 51)
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.616 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6166666666666667
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.748 (+/-0.449) for {'C': 1.0, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.639 (+/-0.326) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.615 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6663461538461538
RBF的粗grid search最佳超参: {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0} RBF的粗grid search最高分值: 0.6166666666666667
linear的粗grid search最佳超参: {'C': 1.0, 'kernel': 'linear'} linear的粗grid search最高分值: 0.6663461538461538
粗grid search得到的parameter是:
[{'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['rbf'], 'gamma': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.664 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.664 (+/-0.388) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'linear'}
0.714 (+/-0.417) for {'C': 10.0, 'kernel': 'linear'}
0.639 (+/-0.326) for {'C': 100.0, 'kernel': 'linear'}
0.639 (+/-0.326) for {'C': 1000.0, 'kernel': 'linear'}
0.639 (+/-0.326) for {'C': 10000.0, 'kernel': 'linear'}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [1]
检查交叉验证集中测试集标签是否有预测不到的值: {-1}
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 1.00 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.236) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.640 (+/-0.323) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.616 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.640 (+/-0.323) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.616 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.640 (+/-0.323) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'linear'}
0.665 (+/-0.314) for {'C': 10.0, 'kernel': 'linear'}
0.615 (+/-0.238) for {'C': 100.0, 'kernel': 'linear'}
0.615 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
0.615 (+/-0.238) for {'C': 10000.0, 'kernel': 'linear'}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 10.0, 'kernel': 'linear'}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.665224358974359
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.672 (+/-0.392) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.689 (+/-0.374) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.673 (+/-0.330) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.664 (+/-0.326) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.664 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.664 (+/-0.326) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.664 (+/-0.388) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.664 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 1.0, 'kernel': 'linear'}
0.689 (+/-0.374) for {'C': 10.0, 'kernel': 'linear'}
0.639 (+/-0.326) for {'C': 100.0, 'kernel': 'linear'}
0.639 (+/-0.326) for {'C': 1000.0, 'kernel': 'linear'}
0.639 (+/-0.326) for {'C': 10000.0, 'kernel': 'linear'}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.641 (+/-0.235) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.315) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.236) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.666 (+/-0.315) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.640 (+/-0.323) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.315) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.640 (+/-0.323) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.666 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.640 (+/-0.323) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.665 (+/-0.315) for {'C': 10.0, 'kernel': 'linear'}
0.615 (+/-0.238) for {'C': 100.0, 'kernel': 'linear'}
0.615 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
0.615 (+/-0.238) for {'C': 10000.0, 'kernel': 'linear'}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6663461538461538
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.664 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.664 (+/-0.388) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.689 (+/-0.436) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.714 (+/-0.418) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.714 (+/-0.417) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [1]
检查交叉验证集中测试集标签是否有预测不到的值: {-1}
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 1.00 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.236) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.640 (+/-0.323) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.616 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.640 (+/-0.323) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.616 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.640 (+/-0.323) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.641 (+/-0.235) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.665 (+/-0.314) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 9.999999999999998, 'kernel': 'linear'}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.665224358974359
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.672 (+/-0.392) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.689 (+/-0.374) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.673 (+/-0.330) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.664 (+/-0.326) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.664 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.664 (+/-0.326) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.664 (+/-0.388) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.664 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.689 (+/-0.382) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.659 (+/-0.313) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.663 (+/-0.372) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.671 (+/-0.376) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.689 (+/-0.374) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.641 (+/-0.235) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.315) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.236) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.666 (+/-0.315) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.640 (+/-0.323) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.315) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.640 (+/-0.323) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.666 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.640 (+/-0.323) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.666 (+/-0.315) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.665 (+/-0.313) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.664 (+/-0.312) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.663 (+/-0.313) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.665 (+/-0.315) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 0.6309573444801931, 'kernel': 'linear'}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6663461538461538
循环迭代之前,delta is: [0.36904266]
执行tunemodel()函数前,使用的grid search parameter是:
[{'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['rbf'], 'gamma': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05])}, {'C': array([0.39810717, 0.43651583, 0.47863009, 0.52480746, 0.57543994,
0.63095734, 0.69183097, 0.75857758, 0.83176377, 0.91201084,
1. ]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.664 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.664 (+/-0.388) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.43651583224016594, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.47863009232263826, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.5248074602497725, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.5754399373371568, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.6918309709189364, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.7585775750291837, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.8317637711026709, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.9120108393559095, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.9999999999999997, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [1]
检查交叉验证集中测试集标签是否有预测不到的值: {-1}
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 1.00 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.236) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.640 (+/-0.323) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.616 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.640 (+/-0.323) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.616 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.640 (+/-0.323) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.43651583224016594, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.47863009232263826, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.5248074602497725, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.5754399373371568, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.6918309709189364, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.7585775750291837, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.8317637711026709, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.9120108393559095, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.9999999999999997, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6400641025641025
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.672 (+/-0.392) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.689 (+/-0.374) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.673 (+/-0.330) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.664 (+/-0.326) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.664 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.664 (+/-0.326) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.664 (+/-0.388) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.664 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.43651583224016594, 'kernel': 'linear'}
0.797 (+/-0.492) for {'C': 0.47863009232263826, 'kernel': 'linear'}
0.772 (+/-0.473) for {'C': 0.5248074602497725, 'kernel': 'linear'}
0.747 (+/-0.449) for {'C': 0.5754399373371568, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.6918309709189364, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.7585775750291837, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.8317637711026709, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.9120108393559095, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.9999999999999997, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.641 (+/-0.235) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.315) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.236) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.666 (+/-0.315) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.640 (+/-0.323) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.315) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.640 (+/-0.323) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.666 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.640 (+/-0.323) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.43651583224016594, 'kernel': 'linear'}
0.642 (+/-0.236) for {'C': 0.47863009232263826, 'kernel': 'linear'}
0.642 (+/-0.236) for {'C': 0.5248074602497725, 'kernel': 'linear'}
0.641 (+/-0.236) for {'C': 0.5754399373371568, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.6918309709189364, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.7585775750291837, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.8317637711026709, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.9120108393559095, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.9999999999999997, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 0.6309573444801931, 'kernel': 'linear'}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6663461538461538
第0轮loop中,tunemodel函数得到的最优参数是: ([{'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['rbf'], 'gamma': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05])}, {'C': array([0.57543994, 0.58613816, 0.59703529, 0.608135 , 0.61944108,
0.63095734, 0.64268772, 0.65463617, 0.66680677, 0.67920363,
0.69183097]), 'kernel': ['linear']}], {'C': 0.6309573444801931, 'kernel': 'linear'}, 0.6663461538461538)
这是第0次迭代微调C和gamma。
第0次迭代,得到delta: [0.]
SVC模型clf_op_iter的参数是: {'kernel': 'linear', 'decision_function_shape': 'ovr', 'class_weight': {-1: 30}, 'tol': 0.001, 'gamma': 'auto', 'random_state': 0, 'cache_size': 200, 'verbose': False, 'degree': 3, 'C': 0.6309573444801931, 'probability': False, 'shrinking': True, 'coef0': 0.0, 'max_iter': -1}
训练模型SVC对训练样本的预测准确率: 0.9029187817258884
测试集中,预测为舞弊样本的有: (array([ 370, 658, 1246, 1247, 1248, 1251, 1252, 1255, 1256], dtype=int64),)
测试集中,实际为舞弊样本的有: (array([1246, 1247, 1248, 1249, 1250, 1251, 1252, 1253, 1254, 1255, 1256],
dtype=int64),)
预测的舞弊样本数目: 9
训练模型SVC对测试样本的预测准确率: 0.9225888324873096
以上是第2次特征筛选。
第2次特征筛选,AUC值是: 0.8173792499635195
X_train_iter_svc.shape is: (1257, 50)
X_test_iter_svc.shape is: (1257, 50)
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.616 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6166666666666667
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.748 (+/-0.449) for {'C': 1.0, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.639 (+/-0.326) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.615 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6663461538461538
RBF的粗grid search最佳超参: {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0} RBF的粗grid search最高分值: 0.6166666666666667
linear的粗grid search最佳超参: {'C': 1.0, 'kernel': 'linear'} linear的粗grid search最高分值: 0.6663461538461538
粗grid search得到的parameter是:
[{'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['rbf'], 'gamma': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.664 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.664 (+/-0.388) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'linear'}
0.714 (+/-0.417) for {'C': 10.0, 'kernel': 'linear'}
0.639 (+/-0.326) for {'C': 100.0, 'kernel': 'linear'}
0.639 (+/-0.326) for {'C': 1000.0, 'kernel': 'linear'}
0.639 (+/-0.326) for {'C': 10000.0, 'kernel': 'linear'}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [1]
检查交叉验证集中测试集标签是否有预测不到的值: {-1}
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 1.00 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.236) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.640 (+/-0.323) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.616 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.640 (+/-0.323) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.616 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.640 (+/-0.323) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'linear'}
0.665 (+/-0.314) for {'C': 10.0, 'kernel': 'linear'}
0.615 (+/-0.238) for {'C': 100.0, 'kernel': 'linear'}
0.615 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
0.615 (+/-0.238) for {'C': 10000.0, 'kernel': 'linear'}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 10.0, 'kernel': 'linear'}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.665224358974359
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.672 (+/-0.392) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.689 (+/-0.374) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.664 (+/-0.317) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.656 (+/-0.312) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.664 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.656 (+/-0.312) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.664 (+/-0.388) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.664 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 1.0, 'kernel': 'linear'}
0.689 (+/-0.374) for {'C': 10.0, 'kernel': 'linear'}
0.639 (+/-0.326) for {'C': 100.0, 'kernel': 'linear'}
0.639 (+/-0.326) for {'C': 1000.0, 'kernel': 'linear'}
0.639 (+/-0.326) for {'C': 10000.0, 'kernel': 'linear'}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.641 (+/-0.235) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.314) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.236) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.665 (+/-0.314) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.640 (+/-0.323) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.665 (+/-0.314) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.640 (+/-0.323) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.666 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.640 (+/-0.323) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.665 (+/-0.315) for {'C': 10.0, 'kernel': 'linear'}
0.615 (+/-0.238) for {'C': 100.0, 'kernel': 'linear'}
0.615 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
0.615 (+/-0.238) for {'C': 10000.0, 'kernel': 'linear'}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6663461538461538
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.664 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.664 (+/-0.388) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.689 (+/-0.436) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.714 (+/-0.418) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.714 (+/-0.417) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [1]
检查交叉验证集中测试集标签是否有预测不到的值: {-1}
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 1.00 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.236) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.640 (+/-0.323) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.616 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.640 (+/-0.323) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.616 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.640 (+/-0.323) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.641 (+/-0.235) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.665 (+/-0.314) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 9.999999999999998, 'kernel': 'linear'}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.665224358974359
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.672 (+/-0.392) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.689 (+/-0.374) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.664 (+/-0.317) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.656 (+/-0.312) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.664 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.656 (+/-0.312) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.664 (+/-0.388) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.664 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.747 (+/-0.449) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.689 (+/-0.382) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.684 (+/-0.372) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.663 (+/-0.372) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.671 (+/-0.376) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.689 (+/-0.374) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.641 (+/-0.235) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.314) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.236) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.665 (+/-0.314) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.640 (+/-0.323) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.665 (+/-0.314) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.640 (+/-0.323) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.666 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.640 (+/-0.323) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.641 (+/-0.236) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.666 (+/-0.315) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.666 (+/-0.314) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.664 (+/-0.312) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.664 (+/-0.313) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.665 (+/-0.315) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 0.9999999999999997, 'kernel': 'linear'}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6663461538461538
循环迭代之前,delta is: [3.33066907e-16]
SVC模型clf_op_iter的参数是: {'kernel': 'linear', 'decision_function_shape': 'ovr', 'class_weight': {-1: 30}, 'tol': 0.001, 'gamma': 'auto', 'random_state': 0, 'cache_size': 200, 'verbose': False, 'degree': 3, 'C': 0.9999999999999997, 'probability': False, 'shrinking': True, 'coef0': 0.0, 'max_iter': -1}
训练模型SVC对训练样本的预测准确率: 0.9022842639593909
测试集中,预测为舞弊样本的有: (array([ 370, 1248, 1252, 1255, 1256], dtype=int64),)
测试集中,实际为舞弊样本的有: (array([1246, 1247, 1248, 1249, 1250, 1251, 1252, 1253, 1254, 1255, 1256],
dtype=int64),)
预测的舞弊样本数目: 5
训练模型SVC对测试样本的预测准确率: 0.8661167512690355
以上是第3次特征筛选。
第3次特征筛选,AUC值是: 0.6814168977090325
X_train_iter_svc.shape is: (1257, 49)
X_test_iter_svc.shape is: (1257, 49)
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.616 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6166666666666667
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.748 (+/-0.449) for {'C': 1.0, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.639 (+/-0.326) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.615 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6663461538461538
RBF的粗grid search最佳超参: {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0} RBF的粗grid search最高分值: 0.6166666666666667
linear的粗grid search最佳超参: {'C': 1.0, 'kernel': 'linear'} linear的粗grid search最高分值: 0.6663461538461538
粗grid search得到的parameter是:
[{'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['rbf'], 'gamma': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.664 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.664 (+/-0.388) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'linear'}
0.714 (+/-0.417) for {'C': 10.0, 'kernel': 'linear'}
0.639 (+/-0.326) for {'C': 100.0, 'kernel': 'linear'}
0.639 (+/-0.326) for {'C': 1000.0, 'kernel': 'linear'}
0.639 (+/-0.326) for {'C': 10000.0, 'kernel': 'linear'}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [1]
检查交叉验证集中测试集标签是否有预测不到的值: {-1}
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 1.00 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.236) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.640 (+/-0.323) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.616 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.640 (+/-0.323) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.616 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.640 (+/-0.323) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'linear'}
0.665 (+/-0.314) for {'C': 10.0, 'kernel': 'linear'}
0.615 (+/-0.238) for {'C': 100.0, 'kernel': 'linear'}
0.615 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
0.615 (+/-0.238) for {'C': 10000.0, 'kernel': 'linear'}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 10.0, 'kernel': 'linear'}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.665224358974359
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.672 (+/-0.392) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.689 (+/-0.374) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.664 (+/-0.317) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.656 (+/-0.312) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.664 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.656 (+/-0.312) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.664 (+/-0.388) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.664 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 1.0, 'kernel': 'linear'}
0.689 (+/-0.374) for {'C': 10.0, 'kernel': 'linear'}
0.639 (+/-0.326) for {'C': 100.0, 'kernel': 'linear'}
0.639 (+/-0.326) for {'C': 1000.0, 'kernel': 'linear'}
0.639 (+/-0.326) for {'C': 10000.0, 'kernel': 'linear'}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.641 (+/-0.235) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.314) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.236) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.665 (+/-0.314) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.640 (+/-0.323) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.665 (+/-0.314) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.640 (+/-0.323) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.666 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.640 (+/-0.323) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.665 (+/-0.315) for {'C': 10.0, 'kernel': 'linear'}
0.615 (+/-0.238) for {'C': 100.0, 'kernel': 'linear'}
0.615 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
0.615 (+/-0.238) for {'C': 10000.0, 'kernel': 'linear'}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6663461538461538
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.664 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.664 (+/-0.388) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.689 (+/-0.436) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.714 (+/-0.418) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.714 (+/-0.417) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [1]
检查交叉验证集中测试集标签是否有预测不到的值: {-1}
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 1.00 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.236) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.640 (+/-0.323) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.616 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.640 (+/-0.323) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.616 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.640 (+/-0.323) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.641 (+/-0.235) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.665 (+/-0.314) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 9.999999999999998, 'kernel': 'linear'}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.665224358974359
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.672 (+/-0.392) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.689 (+/-0.374) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.664 (+/-0.317) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.656 (+/-0.312) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.664 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.656 (+/-0.312) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.664 (+/-0.388) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.664 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.747 (+/-0.449) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.689 (+/-0.382) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.684 (+/-0.372) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.663 (+/-0.372) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.671 (+/-0.376) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.689 (+/-0.374) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.641 (+/-0.235) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.314) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.236) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.665 (+/-0.314) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.640 (+/-0.323) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.665 (+/-0.314) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.640 (+/-0.323) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.666 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.640 (+/-0.323) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.641 (+/-0.236) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.666 (+/-0.315) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.666 (+/-0.314) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.664 (+/-0.312) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.664 (+/-0.313) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.665 (+/-0.315) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 0.9999999999999997, 'kernel': 'linear'}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6663461538461538
循环迭代之前,delta is: [3.33066907e-16]
SVC模型clf_op_iter的参数是: {'kernel': 'linear', 'decision_function_shape': 'ovr', 'class_weight': {-1: 30}, 'tol': 0.001, 'gamma': 'auto', 'random_state': 0, 'cache_size': 200, 'verbose': False, 'degree': 3, 'C': 0.9999999999999997, 'probability': False, 'shrinking': True, 'coef0': 0.0, 'max_iter': -1}
训练模型SVC对训练样本的预测准确率: 0.9022842639593909
测试集中,预测为舞弊样本的有: (array([ 370, 1248, 1252, 1255, 1256], dtype=int64),)
测试集中,实际为舞弊样本的有: (array([1246, 1247, 1248, 1249, 1250, 1251, 1252, 1253, 1254, 1255, 1256],
dtype=int64),)
预测的舞弊样本数目: 5
训练模型SVC对测试样本的预测准确率: 0.8661167512690355
以上是第4次特征筛选。
第4次特征筛选,AUC值是: 0.6814168977090325
X_train_iter_svc.shape is: (1257, 48)
X_test_iter_svc.shape is: (1257, 48)
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.616 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6166666666666667
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.748 (+/-0.449) for {'C': 1.0, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.639 (+/-0.326) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.615 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6663461538461538
RBF的粗grid search最佳超参: {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0} RBF的粗grid search最高分值: 0.6166666666666667
linear的粗grid search最佳超参: {'C': 1.0, 'kernel': 'linear'} linear的粗grid search最高分值: 0.6663461538461538
粗grid search得到的parameter是:
[{'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['rbf'], 'gamma': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.664 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.664 (+/-0.388) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'linear'}
0.714 (+/-0.417) for {'C': 10.0, 'kernel': 'linear'}
0.639 (+/-0.326) for {'C': 100.0, 'kernel': 'linear'}
0.639 (+/-0.326) for {'C': 1000.0, 'kernel': 'linear'}
0.639 (+/-0.326) for {'C': 10000.0, 'kernel': 'linear'}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [1]
检查交叉验证集中测试集标签是否有预测不到的值: {-1}
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 1.00 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.236) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.640 (+/-0.323) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.616 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.640 (+/-0.323) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.616 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.640 (+/-0.323) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'linear'}
0.665 (+/-0.314) for {'C': 10.0, 'kernel': 'linear'}
0.615 (+/-0.238) for {'C': 100.0, 'kernel': 'linear'}
0.615 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
0.615 (+/-0.238) for {'C': 10000.0, 'kernel': 'linear'}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 10.0, 'kernel': 'linear'}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.665224358974359
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.672 (+/-0.392) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.689 (+/-0.374) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.664 (+/-0.317) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.656 (+/-0.312) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.664 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.656 (+/-0.312) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.664 (+/-0.388) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.656 (+/-0.312) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 1.0, 'kernel': 'linear'}
0.689 (+/-0.374) for {'C': 10.0, 'kernel': 'linear'}
0.639 (+/-0.326) for {'C': 100.0, 'kernel': 'linear'}
0.639 (+/-0.326) for {'C': 1000.0, 'kernel': 'linear'}
0.639 (+/-0.326) for {'C': 10000.0, 'kernel': 'linear'}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.641 (+/-0.235) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.314) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.236) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.665 (+/-0.314) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.640 (+/-0.323) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.665 (+/-0.314) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.640 (+/-0.323) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.665 (+/-0.314) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.640 (+/-0.323) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.665 (+/-0.315) for {'C': 10.0, 'kernel': 'linear'}
0.615 (+/-0.238) for {'C': 100.0, 'kernel': 'linear'}
0.615 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
0.615 (+/-0.238) for {'C': 10000.0, 'kernel': 'linear'}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6663461538461538
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.664 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.664 (+/-0.388) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.689 (+/-0.436) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.714 (+/-0.418) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.714 (+/-0.417) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [1]
检查交叉验证集中测试集标签是否有预测不到的值: {-1}
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 1.00 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.236) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.640 (+/-0.323) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.616 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.640 (+/-0.323) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.616 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.640 (+/-0.323) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.641 (+/-0.235) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.665 (+/-0.314) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 9.999999999999998, 'kernel': 'linear'}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.665224358974359
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.672 (+/-0.392) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.689 (+/-0.374) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.664 (+/-0.317) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.656 (+/-0.312) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.664 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.656 (+/-0.312) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.664 (+/-0.388) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.656 (+/-0.312) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.689 (+/-0.382) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.684 (+/-0.372) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.663 (+/-0.372) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.671 (+/-0.376) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.689 (+/-0.374) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.641 (+/-0.235) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.314) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.236) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.665 (+/-0.314) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.640 (+/-0.323) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.665 (+/-0.314) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.640 (+/-0.323) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.665 (+/-0.314) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.640 (+/-0.323) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.666 (+/-0.315) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.666 (+/-0.314) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.664 (+/-0.312) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.664 (+/-0.313) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.665 (+/-0.315) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 0.6309573444801931, 'kernel': 'linear'}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6663461538461538
循环迭代之前,delta is: [0.36904266]
执行tunemodel()函数前,使用的grid search parameter是:
[{'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['rbf'], 'gamma': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05])}, {'C': array([0.39810717, 0.43651583, 0.47863009, 0.52480746, 0.57543994,
0.63095734, 0.69183097, 0.75857758, 0.83176377, 0.91201084,
1. ]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.664 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.664 (+/-0.388) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.43651583224016594, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.47863009232263826, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.5248074602497725, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.5754399373371568, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.6918309709189364, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.7585775750291837, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.8317637711026709, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.9120108393559095, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.9999999999999997, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [1]
检查交叉验证集中测试集标签是否有预测不到的值: {-1}
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 1.00 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.236) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.640 (+/-0.323) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.616 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.640 (+/-0.323) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.616 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.640 (+/-0.323) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.43651583224016594, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.47863009232263826, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.5248074602497725, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.5754399373371568, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.6918309709189364, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.7585775750291837, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.8317637711026709, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.9120108393559095, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.9999999999999997, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6400641025641025
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.672 (+/-0.392) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.689 (+/-0.374) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.664 (+/-0.317) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.656 (+/-0.312) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.664 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.656 (+/-0.312) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.664 (+/-0.388) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.656 (+/-0.312) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.43651583224016594, 'kernel': 'linear'}
0.797 (+/-0.492) for {'C': 0.47863009232263826, 'kernel': 'linear'}
0.772 (+/-0.473) for {'C': 0.5248074602497725, 'kernel': 'linear'}
0.747 (+/-0.449) for {'C': 0.5754399373371568, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.6918309709189364, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.7585775750291837, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.8317637711026709, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.9120108393559095, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.9999999999999997, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.641 (+/-0.235) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.314) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.236) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.665 (+/-0.314) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.640 (+/-0.323) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.665 (+/-0.314) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.640 (+/-0.323) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.665 (+/-0.314) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.640 (+/-0.323) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.43651583224016594, 'kernel': 'linear'}
0.642 (+/-0.236) for {'C': 0.47863009232263826, 'kernel': 'linear'}
0.642 (+/-0.236) for {'C': 0.5248074602497725, 'kernel': 'linear'}
0.641 (+/-0.236) for {'C': 0.5754399373371568, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.6918309709189364, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.7585775750291837, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.8317637711026709, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.9120108393559095, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.9999999999999997, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 0.6309573444801931, 'kernel': 'linear'}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6663461538461538
第0轮loop中,tunemodel函数得到的最优参数是: ([{'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['rbf'], 'gamma': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05])}, {'C': array([0.57543994, 0.58613816, 0.59703529, 0.608135 , 0.61944108,
0.63095734, 0.64268772, 0.65463617, 0.66680677, 0.67920363,
0.69183097]), 'kernel': ['linear']}], {'C': 0.6309573444801931, 'kernel': 'linear'}, 0.6663461538461538)
这是第0次迭代微调C和gamma。
第0次迭代,得到delta: [0.]
SVC模型clf_op_iter的参数是: {'kernel': 'linear', 'decision_function_shape': 'ovr', 'class_weight': {-1: 30}, 'tol': 0.001, 'gamma': 'auto', 'random_state': 0, 'cache_size': 200, 'verbose': False, 'degree': 3, 'C': 0.6309573444801931, 'probability': False, 'shrinking': True, 'coef0': 0.0, 'max_iter': -1}
训练模型SVC对训练样本的预测准确率: 0.9029187817258884
测试集中,预测为舞弊样本的有: (array([ 370, 658, 1246, 1247, 1248, 1251, 1252, 1255, 1256], dtype=int64),)
测试集中,实际为舞弊样本的有: (array([1246, 1247, 1248, 1249, 1250, 1251, 1252, 1253, 1254, 1255, 1256],
dtype=int64),)
预测的舞弊样本数目: 9
训练模型SVC对测试样本的预测准确率: 0.9225888324873096
以上是第5次特征筛选。
第5次特征筛选,AUC值是: 0.8173792499635195
X_train_iter_svc.shape is: (1257, 47)
X_test_iter_svc.shape is: (1257, 47)
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.616 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6166666666666667
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.748 (+/-0.449) for {'C': 1.0, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.639 (+/-0.326) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.615 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6663461538461538
RBF的粗grid search最佳超参: {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0} RBF的粗grid search最高分值: 0.6166666666666667
linear的粗grid search最佳超参: {'C': 1.0, 'kernel': 'linear'} linear的粗grid search最高分值: 0.6663461538461538
粗grid search得到的parameter是:
[{'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['rbf'], 'gamma': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.664 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.664 (+/-0.388) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'linear'}
0.714 (+/-0.417) for {'C': 10.0, 'kernel': 'linear'}
0.639 (+/-0.326) for {'C': 100.0, 'kernel': 'linear'}
0.639 (+/-0.326) for {'C': 1000.0, 'kernel': 'linear'}
0.639 (+/-0.326) for {'C': 10000.0, 'kernel': 'linear'}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [1]
检查交叉验证集中测试集标签是否有预测不到的值: {-1}
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 1.00 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.640 (+/-0.323) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.616 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.640 (+/-0.323) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.616 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.640 (+/-0.324) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'linear'}
0.665 (+/-0.314) for {'C': 10.0, 'kernel': 'linear'}
0.615 (+/-0.238) for {'C': 100.0, 'kernel': 'linear'}
0.615 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
0.615 (+/-0.238) for {'C': 10000.0, 'kernel': 'linear'}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.14 0.17 0.15 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 10.0, 'kernel': 'linear'}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.665224358974359
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.672 (+/-0.392) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.689 (+/-0.374) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.681 (+/-0.372) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.681 (+/-0.372) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.664 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.681 (+/-0.372) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.664 (+/-0.388) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.689 (+/-0.382) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 1.0, 'kernel': 'linear'}
0.687 (+/-0.375) for {'C': 10.0, 'kernel': 'linear'}
0.639 (+/-0.326) for {'C': 100.0, 'kernel': 'linear'}
0.639 (+/-0.326) for {'C': 1000.0, 'kernel': 'linear'}
0.639 (+/-0.326) for {'C': 10000.0, 'kernel': 'linear'}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.641 (+/-0.235) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.314) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.666 (+/-0.314) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.640 (+/-0.323) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.314) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.640 (+/-0.323) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.666 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.640 (+/-0.324) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.664 (+/-0.314) for {'C': 10.0, 'kernel': 'linear'}
0.615 (+/-0.238) for {'C': 100.0, 'kernel': 'linear'}
0.615 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
0.615 (+/-0.238) for {'C': 10000.0, 'kernel': 'linear'}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6663461538461538
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.664 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.664 (+/-0.388) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.689 (+/-0.436) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.714 (+/-0.418) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.714 (+/-0.417) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [1]
检查交叉验证集中测试集标签是否有预测不到的值: {-1}
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 1.00 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.640 (+/-0.323) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.616 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.640 (+/-0.323) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.616 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.640 (+/-0.324) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.641 (+/-0.235) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.665 (+/-0.314) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.14 0.17 0.15 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 9.999999999999998, 'kernel': 'linear'}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.665224358974359
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.672 (+/-0.392) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.689 (+/-0.374) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.681 (+/-0.372) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.681 (+/-0.372) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.664 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.681 (+/-0.372) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.664 (+/-0.388) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.689 (+/-0.382) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.689 (+/-0.382) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.684 (+/-0.372) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.662 (+/-0.373) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.671 (+/-0.376) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.687 (+/-0.375) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.641 (+/-0.235) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.314) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.666 (+/-0.314) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.640 (+/-0.323) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.314) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.640 (+/-0.323) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.666 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.640 (+/-0.324) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.666 (+/-0.315) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.666 (+/-0.314) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.664 (+/-0.312) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.663 (+/-0.313) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.664 (+/-0.314) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 0.6309573444801931, 'kernel': 'linear'}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6663461538461538
循环迭代之前,delta is: [0.36904266]
执行tunemodel()函数前,使用的grid search parameter是:
[{'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['rbf'], 'gamma': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05])}, {'C': array([0.39810717, 0.43651583, 0.47863009, 0.52480746, 0.57543994,
0.63095734, 0.69183097, 0.75857758, 0.83176377, 0.91201084,
1. ]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.664 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.664 (+/-0.388) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.43651583224016594, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.47863009232263826, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.5248074602497725, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.5754399373371568, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.6918309709189364, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.7585775750291837, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.8317637711026709, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.9120108393559095, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.9999999999999997, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [1]
检查交叉验证集中测试集标签是否有预测不到的值: {-1}
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 1.00 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.640 (+/-0.323) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.616 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.640 (+/-0.323) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.616 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.640 (+/-0.324) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.43651583224016594, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.47863009232263826, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.5248074602497725, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.5754399373371568, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.6918309709189364, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.7585775750291837, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.8317637711026709, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.9120108393559095, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.9999999999999997, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6400641025641025
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.672 (+/-0.392) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.689 (+/-0.374) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.681 (+/-0.372) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.681 (+/-0.372) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.664 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.681 (+/-0.372) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.664 (+/-0.388) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.689 (+/-0.382) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.43651583224016594, 'kernel': 'linear'}
0.797 (+/-0.492) for {'C': 0.47863009232263826, 'kernel': 'linear'}
0.772 (+/-0.473) for {'C': 0.5248074602497725, 'kernel': 'linear'}
0.747 (+/-0.449) for {'C': 0.5754399373371568, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.6918309709189364, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.7585775750291837, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.8317637711026709, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.9120108393559095, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.9999999999999997, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.641 (+/-0.235) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.314) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.666 (+/-0.314) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.640 (+/-0.323) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.314) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.640 (+/-0.323) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.666 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.640 (+/-0.324) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.229) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.43651583224016594, 'kernel': 'linear'}
0.642 (+/-0.236) for {'C': 0.47863009232263826, 'kernel': 'linear'}
0.642 (+/-0.236) for {'C': 0.5248074602497725, 'kernel': 'linear'}
0.641 (+/-0.236) for {'C': 0.5754399373371568, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.6918309709189364, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.7585775750291837, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.8317637711026709, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.9120108393559095, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.9999999999999997, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 0.6309573444801931, 'kernel': 'linear'}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6663461538461538
第0轮loop中,tunemodel函数得到的最优参数是: ([{'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['rbf'], 'gamma': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05])}, {'C': array([0.57543994, 0.58613816, 0.59703529, 0.608135 , 0.61944108,
0.63095734, 0.64268772, 0.65463617, 0.66680677, 0.67920363,
0.69183097]), 'kernel': ['linear']}], {'C': 0.6309573444801931, 'kernel': 'linear'}, 0.6663461538461538)
这是第0次迭代微调C和gamma。
第0次迭代,得到delta: [0.]
SVC模型clf_op_iter的参数是: {'kernel': 'linear', 'decision_function_shape': 'ovr', 'class_weight': {-1: 30}, 'tol': 0.001, 'gamma': 'auto', 'random_state': 0, 'cache_size': 200, 'verbose': False, 'degree': 3, 'C': 0.6309573444801931, 'probability': False, 'shrinking': True, 'coef0': 0.0, 'max_iter': -1}
训练模型SVC对训练样本的预测准确率: 0.9029187817258884
测试集中,预测为舞弊样本的有: (array([ 370, 658, 1246, 1247, 1248, 1251, 1252, 1255, 1256], dtype=int64),)
测试集中,实际为舞弊样本的有: (array([1246, 1247, 1248, 1249, 1250, 1251, 1252, 1253, 1254, 1255, 1256],
dtype=int64),)
预测的舞弊样本数目: 9
训练模型SVC对测试样本的预测准确率: 0.9225888324873096
以上是第6次特征筛选。
第6次特征筛选,AUC值是: 0.8173792499635195
X_train_iter_svc.shape is: (1257, 46)
X_test_iter_svc.shape is: (1257, 46)
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.722 (+/-0.473) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.616 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6166666666666667
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.723 (+/-0.417) for {'C': 1.0, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.635 (+/-0.326) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.615 (+/-0.237) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6661858974358974
RBF的粗grid search最佳超参: {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0} RBF的粗grid search最高分值: 0.6166666666666667
linear的粗grid search最佳超参: {'C': 1.0, 'kernel': 'linear'} linear的粗grid search最高分值: 0.6661858974358974
粗grid search得到的parameter是:
[{'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['rbf'], 'gamma': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.662 (+/-0.389) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.662 (+/-0.389) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.626 (+/-0.318) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.635 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.626 (+/-0.318) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'linear'}
0.714 (+/-0.417) for {'C': 10.0, 'kernel': 'linear'}
0.639 (+/-0.326) for {'C': 100.0, 'kernel': 'linear'}
0.635 (+/-0.326) for {'C': 1000.0, 'kernel': 'linear'}
0.635 (+/-0.326) for {'C': 10000.0, 'kernel': 'linear'}
0.635 (+/-0.326) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [1]
检查交叉验证集中测试集标签是否有预测不到的值: {-1}
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 1.00 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.640 (+/-0.323) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.616 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.640 (+/-0.323) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.616 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.640 (+/-0.323) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'linear'}
0.665 (+/-0.315) for {'C': 10.0, 'kernel': 'linear'}
0.615 (+/-0.238) for {'C': 100.0, 'kernel': 'linear'}
0.615 (+/-0.237) for {'C': 1000.0, 'kernel': 'linear'}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'linear'}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.14 0.17 0.15 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 10.0, 'kernel': 'linear'}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6650641025641026
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.672 (+/-0.392) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.698 (+/-0.383) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.681 (+/-0.372) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.681 (+/-0.372) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.662 (+/-0.389) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.681 (+/-0.372) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.662 (+/-0.389) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.626 (+/-0.318) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.698 (+/-0.383) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.635 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.626 (+/-0.318) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.723 (+/-0.417) for {'C': 1.0, 'kernel': 'linear'}
0.679 (+/-0.373) for {'C': 10.0, 'kernel': 'linear'}
0.639 (+/-0.326) for {'C': 100.0, 'kernel': 'linear'}
0.635 (+/-0.326) for {'C': 1000.0, 'kernel': 'linear'}
0.635 (+/-0.326) for {'C': 10000.0, 'kernel': 'linear'}
0.635 (+/-0.326) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [1]
检查交叉验证集中测试集标签是否有预测不到的值: {-1}
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 1.00 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.666 (+/-0.315) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.314) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.666 (+/-0.314) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.640 (+/-0.323) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.314) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.640 (+/-0.323) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.666 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.640 (+/-0.323) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.664 (+/-0.314) for {'C': 10.0, 'kernel': 'linear'}
0.615 (+/-0.238) for {'C': 100.0, 'kernel': 'linear'}
0.615 (+/-0.237) for {'C': 1000.0, 'kernel': 'linear'}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'linear'}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6661858974358974
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.662 (+/-0.389) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.662 (+/-0.389) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.626 (+/-0.318) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.635 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.626 (+/-0.318) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.689 (+/-0.436) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.714 (+/-0.418) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.714 (+/-0.417) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [1]
检查交叉验证集中测试集标签是否有预测不到的值: {-1}
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 1.00 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.640 (+/-0.323) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.616 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.640 (+/-0.323) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.616 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.640 (+/-0.323) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.641 (+/-0.235) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.665 (+/-0.315) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.14 0.17 0.15 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 9.999999999999998, 'kernel': 'linear'}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6650641025641026
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.672 (+/-0.392) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.698 (+/-0.383) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.681 (+/-0.372) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.681 (+/-0.372) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.662 (+/-0.389) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.681 (+/-0.372) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.662 (+/-0.389) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.626 (+/-0.318) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.698 (+/-0.383) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.635 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.626 (+/-0.318) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.797 (+/-0.492) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.723 (+/-0.417) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.689 (+/-0.382) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.684 (+/-0.372) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.668 (+/-0.371) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.670 (+/-0.377) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.679 (+/-0.373) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.666 (+/-0.315) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.314) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.666 (+/-0.314) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.640 (+/-0.323) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.314) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.640 (+/-0.323) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.666 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.640 (+/-0.323) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.642 (+/-0.236) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.666 (+/-0.315) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.666 (+/-0.314) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.664 (+/-0.313) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.663 (+/-0.314) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.664 (+/-0.314) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 0.6309573444801931, 'kernel': 'linear'}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6663461538461538
循环迭代之前,delta is: [0.36904266]
执行tunemodel()函数前,使用的grid search parameter是:
[{'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['rbf'], 'gamma': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05])}, {'C': array([0.39810717, 0.43651583, 0.47863009, 0.52480746, 0.57543994,
0.63095734, 0.69183097, 0.75857758, 0.83176377, 0.91201084,
1. ]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.662 (+/-0.389) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.662 (+/-0.389) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.626 (+/-0.318) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.635 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.626 (+/-0.318) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.43651583224016594, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.47863009232263826, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.5248074602497725, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.5754399373371568, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.6918309709189364, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.7585775750291837, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.8317637711026709, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.9120108393559095, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.9999999999999997, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [1]
检查交叉验证集中测试集标签是否有预测不到的值: {-1}
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 1.00 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.640 (+/-0.323) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.616 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.640 (+/-0.323) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.616 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.640 (+/-0.323) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.43651583224016594, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.47863009232263826, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.5248074602497725, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.5754399373371568, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.6918309709189364, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.7585775750291837, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.8317637711026709, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.9120108393559095, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.9999999999999997, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6400641025641025
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.672 (+/-0.392) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.698 (+/-0.383) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.681 (+/-0.372) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.681 (+/-0.372) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.662 (+/-0.389) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.681 (+/-0.372) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.662 (+/-0.389) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.626 (+/-0.318) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.698 (+/-0.383) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.635 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.626 (+/-0.318) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.797 (+/-0.492) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.797 (+/-0.492) for {'C': 0.43651583224016594, 'kernel': 'linear'}
0.797 (+/-0.492) for {'C': 0.47863009232263826, 'kernel': 'linear'}
0.772 (+/-0.473) for {'C': 0.5248074602497725, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.5754399373371568, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.6918309709189364, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.7585775750291837, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.8317637711026709, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.9120108393559095, 'kernel': 'linear'}
0.723 (+/-0.417) for {'C': 0.9999999999999997, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.666 (+/-0.315) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.314) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.666 (+/-0.314) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.640 (+/-0.323) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.314) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.640 (+/-0.323) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.666 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.640 (+/-0.323) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.642 (+/-0.236) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.642 (+/-0.236) for {'C': 0.43651583224016594, 'kernel': 'linear'}
0.642 (+/-0.236) for {'C': 0.47863009232263826, 'kernel': 'linear'}
0.642 (+/-0.236) for {'C': 0.5248074602497725, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.5754399373371568, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.6918309709189364, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.7585775750291837, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.8317637711026709, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.9120108393559095, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.9999999999999997, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 0.5754399373371568, 'kernel': 'linear'}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6663461538461538
第0轮loop中,tunemodel函数得到的最优参数是: ([{'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['rbf'], 'gamma': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05])}, {'C': array([0.52480746, 0.53456436, 0.54450265, 0.55462571, 0.56493697,
0.57543994, 0.58613816, 0.59703529, 0.608135 , 0.61944108,
0.63095734]), 'kernel': ['linear']}], {'C': 0.5754399373371568, 'kernel': 'linear'}, 0.6663461538461538)
这是第0次迭代微调C和gamma。
第0次迭代,得到delta: [0.05551741]
执行tunemodel()函数前,使用的grid search parameter是:
[{'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['rbf'], 'gamma': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05])}, {'C': array([0.52480746, 0.53456436, 0.54450265, 0.55462571, 0.56493697,
0.57543994, 0.58613816, 0.59703529, 0.608135 , 0.61944108,
0.63095734]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.662 (+/-0.389) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.662 (+/-0.389) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.626 (+/-0.318) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.635 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.626 (+/-0.318) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 0.5248074602497725, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.5345643593969717, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.5445026528424212, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.5546257129579107, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.5649369748123024, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.5754399373371568, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.5861381645140287, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.5970352865838368, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.6081350012787178, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.6194410750767814, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.6309573444801931, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [1]
检查交叉验证集中测试集标签是否有预测不到的值: {-1}
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 1.00 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.640 (+/-0.323) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.616 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.640 (+/-0.323) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.616 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.640 (+/-0.323) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 0.5248074602497725, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.5345643593969717, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.5445026528424212, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.5546257129579107, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.5649369748123024, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.5754399373371568, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.5861381645140287, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.5970352865838368, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.6081350012787178, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.6194410750767814, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.6309573444801931, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6400641025641025
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.672 (+/-0.392) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.698 (+/-0.383) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.681 (+/-0.372) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.681 (+/-0.372) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.662 (+/-0.389) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.681 (+/-0.372) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.662 (+/-0.389) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.626 (+/-0.318) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.698 (+/-0.383) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.635 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.626 (+/-0.318) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.772 (+/-0.473) for {'C': 0.5248074602497725, 'kernel': 'linear'}
0.773 (+/-0.474) for {'C': 0.5345643593969717, 'kernel': 'linear'}
0.773 (+/-0.474) for {'C': 0.5445026528424212, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.5546257129579107, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.5649369748123024, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.5754399373371568, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.5861381645140287, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.5970352865838368, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.6081350012787178, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.6194410750767814, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.6309573444801931, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.666 (+/-0.315) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.314) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.666 (+/-0.314) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.640 (+/-0.323) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.314) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.640 (+/-0.323) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.666 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.640 (+/-0.323) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.642 (+/-0.236) for {'C': 0.5248074602497725, 'kernel': 'linear'}
0.667 (+/-0.316) for {'C': 0.5345643593969717, 'kernel': 'linear'}
0.667 (+/-0.316) for {'C': 0.5445026528424212, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.5546257129579107, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.5649369748123024, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.5754399373371568, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.5861381645140287, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.5970352865838368, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.6081350012787178, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.6194410750767814, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.6309573444801931, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 0.5345643593969717, 'kernel': 'linear'}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6665064102564102
第1轮loop中,tunemodel函数得到的最优参数是: ([{'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['rbf'], 'gamma': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05])}, {'C': array([0.52480746, 0.52674449, 0.52868867, 0.53064002, 0.53259857,
0.53456436, 0.5365374 , 0.53851772, 0.54050535, 0.54250032,
0.54450265]), 'kernel': ['linear']}], {'C': 0.5345643593969717, 'kernel': 'linear'}, 0.6665064102564102)
这是第1次迭代微调C和gamma。
第1次迭代,得到delta: [0.04087558]
执行tunemodel()函数前,使用的grid search parameter是:
[{'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['rbf'], 'gamma': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05])}, {'C': array([0.52480746, 0.52674449, 0.52868867, 0.53064002, 0.53259857,
0.53456436, 0.5365374 , 0.53851772, 0.54050535, 0.54250032,
0.54450265]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.662 (+/-0.389) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.662 (+/-0.389) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.626 (+/-0.318) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.635 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.626 (+/-0.318) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 0.5248074602497725, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.526744488331906, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.5286886658508809, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.5306400191947748, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.5325985748490616, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.5345643593969717, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.5365373995198517, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.538517721997528, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.5405053537086689, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.5425003216311501, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.5445026528424212, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [1]
检查交叉验证集中测试集标签是否有预测不到的值: {-1}
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 1.00 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.640 (+/-0.323) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.616 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.640 (+/-0.323) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.616 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.640 (+/-0.323) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 0.5248074602497725, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.526744488331906, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.5286886658508809, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.5306400191947748, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.5325985748490616, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.5345643593969717, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.5365373995198517, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.538517721997528, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.5405053537086689, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.5425003216311501, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.5445026528424212, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6400641025641025
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.672 (+/-0.392) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.698 (+/-0.383) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.681 (+/-0.372) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.681 (+/-0.372) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.662 (+/-0.389) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.681 (+/-0.372) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.662 (+/-0.389) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.626 (+/-0.318) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.698 (+/-0.383) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.635 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.626 (+/-0.318) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.772 (+/-0.473) for {'C': 0.5248074602497725, 'kernel': 'linear'}
0.772 (+/-0.473) for {'C': 0.526744488331906, 'kernel': 'linear'}
0.772 (+/-0.473) for {'C': 0.5286886658508809, 'kernel': 'linear'}
0.773 (+/-0.474) for {'C': 0.5306400191947748, 'kernel': 'linear'}
0.773 (+/-0.474) for {'C': 0.5325985748490616, 'kernel': 'linear'}
0.773 (+/-0.474) for {'C': 0.5345643593969717, 'kernel': 'linear'}
0.773 (+/-0.474) for {'C': 0.5365373995198517, 'kernel': 'linear'}
0.773 (+/-0.474) for {'C': 0.538517721997528, 'kernel': 'linear'}
0.773 (+/-0.474) for {'C': 0.5405053537086689, 'kernel': 'linear'}
0.773 (+/-0.474) for {'C': 0.5425003216311501, 'kernel': 'linear'}
0.773 (+/-0.474) for {'C': 0.5445026528424212, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.666 (+/-0.315) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.314) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.666 (+/-0.314) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.640 (+/-0.323) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.314) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.640 (+/-0.323) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.666 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.640 (+/-0.323) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.642 (+/-0.236) for {'C': 0.5248074602497725, 'kernel': 'linear'}
0.642 (+/-0.236) for {'C': 0.526744488331906, 'kernel': 'linear'}
0.642 (+/-0.236) for {'C': 0.5286886658508809, 'kernel': 'linear'}
0.667 (+/-0.316) for {'C': 0.5306400191947748, 'kernel': 'linear'}
0.667 (+/-0.316) for {'C': 0.5325985748490616, 'kernel': 'linear'}
0.667 (+/-0.316) for {'C': 0.5345643593969717, 'kernel': 'linear'}
0.667 (+/-0.316) for {'C': 0.5365373995198517, 'kernel': 'linear'}
0.667 (+/-0.316) for {'C': 0.538517721997528, 'kernel': 'linear'}
0.667 (+/-0.316) for {'C': 0.5405053537086689, 'kernel': 'linear'}
0.667 (+/-0.316) for {'C': 0.5425003216311501, 'kernel': 'linear'}
0.667 (+/-0.316) for {'C': 0.5445026528424212, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 0.5306400191947748, 'kernel': 'linear'}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6665064102564102
第2轮loop中,tunemodel函数得到的最优参数是: ([{'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['rbf'], 'gamma': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05])}, {'C': array([0.52868867, 0.52907836, 0.52946834, 0.52985862, 0.53024917,
0.53064002, 0.53103115, 0.53142258, 0.53181429, 0.53220629,
0.53259857]), 'kernel': ['linear']}], {'C': 0.5306400191947748, 'kernel': 'linear'}, 0.6665064102564102)
这是第2次迭代微调C和gamma。
第2次迭代,得到delta: [0.00392434]
SVC模型clf_op_iter的参数是: {'kernel': 'linear', 'decision_function_shape': 'ovr', 'class_weight': {-1: 30}, 'tol': 0.001, 'gamma': 'auto', 'random_state': 0, 'cache_size': 200, 'verbose': False, 'degree': 3, 'C': 0.5306400191947748, 'probability': False, 'shrinking': True, 'coef0': 0.0, 'max_iter': -1}
训练模型SVC对训练样本的预测准确率: 0.9029187817258884
测试集中,预测为舞弊样本的有: (array([ 370, 658, 769, 1246, 1247, 1248, 1251, 1252, 1255, 1256],
dtype=int64),)
测试集中,实际为舞弊样本的有: (array([1246, 1247, 1248, 1249, 1250, 1251, 1252, 1253, 1254, 1255, 1256],
dtype=int64),)
预测的舞弊样本数目: 10
训练模型SVC对测试样本的预测准确率: 0.9219543147208121
以上是第7次特征筛选。
第7次特征筛选,AUC值是: 0.8169779658543703
X_train_iter_svc.shape is: (1257, 45)
X_test_iter_svc.shape is: (1257, 45)
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.722 (+/-0.473) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.616 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6166666666666667
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.723 (+/-0.417) for {'C': 1.0, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.635 (+/-0.326) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.615 (+/-0.237) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6661858974358974
RBF的粗grid search最佳超参: {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0} RBF的粗grid search最高分值: 0.6166666666666667
linear的粗grid search最佳超参: {'C': 1.0, 'kernel': 'linear'} linear的粗grid search最高分值: 0.6661858974358974
粗grid search得到的parameter是:
[{'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['rbf'], 'gamma': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.662 (+/-0.389) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.662 (+/-0.389) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.626 (+/-0.318) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.635 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.626 (+/-0.318) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'linear'}
0.714 (+/-0.417) for {'C': 10.0, 'kernel': 'linear'}
0.639 (+/-0.326) for {'C': 100.0, 'kernel': 'linear'}
0.635 (+/-0.326) for {'C': 1000.0, 'kernel': 'linear'}
0.635 (+/-0.326) for {'C': 10000.0, 'kernel': 'linear'}
0.635 (+/-0.326) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [1]
检查交叉验证集中测试集标签是否有预测不到的值: {-1}
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 1.00 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.640 (+/-0.323) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.616 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.640 (+/-0.323) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.616 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.640 (+/-0.323) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'linear'}
0.665 (+/-0.315) for {'C': 10.0, 'kernel': 'linear'}
0.615 (+/-0.238) for {'C': 100.0, 'kernel': 'linear'}
0.615 (+/-0.237) for {'C': 1000.0, 'kernel': 'linear'}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'linear'}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.14 0.17 0.15 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 10.0, 'kernel': 'linear'}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6650641025641026
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.672 (+/-0.392) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.698 (+/-0.383) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.681 (+/-0.372) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.681 (+/-0.372) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.662 (+/-0.389) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.681 (+/-0.372) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.662 (+/-0.389) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.626 (+/-0.318) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.698 (+/-0.383) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.635 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.626 (+/-0.318) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.723 (+/-0.417) for {'C': 1.0, 'kernel': 'linear'}
0.679 (+/-0.373) for {'C': 10.0, 'kernel': 'linear'}
0.639 (+/-0.326) for {'C': 100.0, 'kernel': 'linear'}
0.635 (+/-0.326) for {'C': 1000.0, 'kernel': 'linear'}
0.635 (+/-0.326) for {'C': 10000.0, 'kernel': 'linear'}
0.635 (+/-0.326) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [1]
检查交叉验证集中测试集标签是否有预测不到的值: {-1}
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 1.00 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.666 (+/-0.315) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.314) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.666 (+/-0.314) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.640 (+/-0.323) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.314) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.640 (+/-0.323) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.666 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.640 (+/-0.323) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.664 (+/-0.314) for {'C': 10.0, 'kernel': 'linear'}
0.615 (+/-0.238) for {'C': 100.0, 'kernel': 'linear'}
0.615 (+/-0.237) for {'C': 1000.0, 'kernel': 'linear'}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'linear'}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6661858974358974
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.662 (+/-0.389) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.662 (+/-0.389) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.626 (+/-0.318) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.635 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.626 (+/-0.318) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.689 (+/-0.436) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.714 (+/-0.418) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.714 (+/-0.417) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [1]
检查交叉验证集中测试集标签是否有预测不到的值: {-1}
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 1.00 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.640 (+/-0.323) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.616 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.640 (+/-0.323) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.616 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.640 (+/-0.323) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.641 (+/-0.235) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.665 (+/-0.315) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.14 0.17 0.15 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 9.999999999999998, 'kernel': 'linear'}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6650641025641026
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.672 (+/-0.392) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.698 (+/-0.383) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.681 (+/-0.372) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.681 (+/-0.372) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.662 (+/-0.389) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.681 (+/-0.372) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.662 (+/-0.389) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.626 (+/-0.318) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.698 (+/-0.383) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.635 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.626 (+/-0.318) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.797 (+/-0.492) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.723 (+/-0.417) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.689 (+/-0.382) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.684 (+/-0.372) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.668 (+/-0.371) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.670 (+/-0.377) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.679 (+/-0.373) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.666 (+/-0.315) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.314) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.666 (+/-0.314) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.640 (+/-0.323) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.314) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.640 (+/-0.323) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.666 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.640 (+/-0.323) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.642 (+/-0.236) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.666 (+/-0.315) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.666 (+/-0.314) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.664 (+/-0.313) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.663 (+/-0.314) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.664 (+/-0.314) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 0.6309573444801931, 'kernel': 'linear'}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6663461538461538
循环迭代之前,delta is: [0.36904266]
执行tunemodel()函数前,使用的grid search parameter是:
[{'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['rbf'], 'gamma': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05])}, {'C': array([0.39810717, 0.43651583, 0.47863009, 0.52480746, 0.57543994,
0.63095734, 0.69183097, 0.75857758, 0.83176377, 0.91201084,
1. ]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.662 (+/-0.389) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.662 (+/-0.389) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.626 (+/-0.318) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.635 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.626 (+/-0.318) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.43651583224016594, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.47863009232263826, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.5248074602497725, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.5754399373371568, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.6918309709189364, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.7585775750291837, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.8317637711026709, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.9120108393559095, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.9999999999999997, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [1]
检查交叉验证集中测试集标签是否有预测不到的值: {-1}
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 1.00 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.640 (+/-0.323) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.616 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.640 (+/-0.323) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.616 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.640 (+/-0.323) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.43651583224016594, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.47863009232263826, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.5248074602497725, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.5754399373371568, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.6918309709189364, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.7585775750291837, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.8317637711026709, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.9120108393559095, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.9999999999999997, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6400641025641025
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.672 (+/-0.392) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.698 (+/-0.383) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.681 (+/-0.372) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.681 (+/-0.372) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.662 (+/-0.389) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.681 (+/-0.372) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.662 (+/-0.389) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.626 (+/-0.318) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.698 (+/-0.383) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.635 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.626 (+/-0.318) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.797 (+/-0.492) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.797 (+/-0.492) for {'C': 0.43651583224016594, 'kernel': 'linear'}
0.797 (+/-0.492) for {'C': 0.47863009232263826, 'kernel': 'linear'}
0.772 (+/-0.473) for {'C': 0.5248074602497725, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.5754399373371568, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.6918309709189364, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.7585775750291837, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.8317637711026709, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.9120108393559095, 'kernel': 'linear'}
0.723 (+/-0.417) for {'C': 0.9999999999999997, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.666 (+/-0.315) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.314) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.666 (+/-0.314) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.640 (+/-0.323) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.314) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.640 (+/-0.323) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.666 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.640 (+/-0.323) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.642 (+/-0.236) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.642 (+/-0.236) for {'C': 0.43651583224016594, 'kernel': 'linear'}
0.642 (+/-0.236) for {'C': 0.47863009232263826, 'kernel': 'linear'}
0.642 (+/-0.236) for {'C': 0.5248074602497725, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.5754399373371568, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.6918309709189364, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.7585775750291837, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.8317637711026709, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.9120108393559095, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.9999999999999997, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 0.5754399373371568, 'kernel': 'linear'}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6663461538461538
第0轮loop中,tunemodel函数得到的最优参数是: ([{'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['rbf'], 'gamma': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05])}, {'C': array([0.52480746, 0.53456436, 0.54450265, 0.55462571, 0.56493697,
0.57543994, 0.58613816, 0.59703529, 0.608135 , 0.61944108,
0.63095734]), 'kernel': ['linear']}], {'C': 0.5754399373371568, 'kernel': 'linear'}, 0.6663461538461538)
这是第0次迭代微调C和gamma。
第0次迭代,得到delta: [0.05551741]
执行tunemodel()函数前,使用的grid search parameter是:
[{'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['rbf'], 'gamma': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05])}, {'C': array([0.52480746, 0.53456436, 0.54450265, 0.55462571, 0.56493697,
0.57543994, 0.58613816, 0.59703529, 0.608135 , 0.61944108,
0.63095734]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.662 (+/-0.389) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.662 (+/-0.389) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.626 (+/-0.318) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.635 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.626 (+/-0.318) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 0.5248074602497725, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.5345643593969717, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.5445026528424212, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.5546257129579107, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.5649369748123024, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.5754399373371568, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.5861381645140287, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.5970352865838368, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.6081350012787178, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.6194410750767814, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.6309573444801931, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [1]
检查交叉验证集中测试集标签是否有预测不到的值: {-1}
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 1.00 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.640 (+/-0.323) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.616 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.640 (+/-0.323) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.616 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.640 (+/-0.323) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 0.5248074602497725, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.5345643593969717, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.5445026528424212, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.5546257129579107, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.5649369748123024, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.5754399373371568, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.5861381645140287, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.5970352865838368, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.6081350012787178, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.6194410750767814, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.6309573444801931, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6400641025641025
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.672 (+/-0.392) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.698 (+/-0.383) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.681 (+/-0.372) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.681 (+/-0.372) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.662 (+/-0.389) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.681 (+/-0.372) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.662 (+/-0.389) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.626 (+/-0.318) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.698 (+/-0.383) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.635 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.626 (+/-0.318) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.772 (+/-0.473) for {'C': 0.5248074602497725, 'kernel': 'linear'}
0.773 (+/-0.474) for {'C': 0.5345643593969717, 'kernel': 'linear'}
0.773 (+/-0.474) for {'C': 0.5445026528424212, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.5546257129579107, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.5649369748123024, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.5754399373371568, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.5861381645140287, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.5970352865838368, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.6081350012787178, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.6194410750767814, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.6309573444801931, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.666 (+/-0.315) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.314) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.666 (+/-0.314) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.640 (+/-0.323) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.314) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.640 (+/-0.323) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.666 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.640 (+/-0.323) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.642 (+/-0.236) for {'C': 0.5248074602497725, 'kernel': 'linear'}
0.667 (+/-0.316) for {'C': 0.5345643593969717, 'kernel': 'linear'}
0.667 (+/-0.316) for {'C': 0.5445026528424212, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.5546257129579107, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.5649369748123024, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.5754399373371568, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.5861381645140287, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.5970352865838368, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.6081350012787178, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.6194410750767814, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.6309573444801931, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 0.5345643593969717, 'kernel': 'linear'}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6665064102564102
第1轮loop中,tunemodel函数得到的最优参数是: ([{'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['rbf'], 'gamma': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05])}, {'C': array([0.52480746, 0.52674449, 0.52868867, 0.53064002, 0.53259857,
0.53456436, 0.5365374 , 0.53851772, 0.54050535, 0.54250032,
0.54450265]), 'kernel': ['linear']}], {'C': 0.5345643593969717, 'kernel': 'linear'}, 0.6665064102564102)
这是第1次迭代微调C和gamma。
第1次迭代,得到delta: [0.04087558]
执行tunemodel()函数前,使用的grid search parameter是:
[{'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['rbf'], 'gamma': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05])}, {'C': array([0.52480746, 0.52674449, 0.52868867, 0.53064002, 0.53259857,
0.53456436, 0.5365374 , 0.53851772, 0.54050535, 0.54250032,
0.54450265]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.662 (+/-0.389) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.662 (+/-0.389) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.626 (+/-0.318) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.635 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.626 (+/-0.318) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 0.5248074602497725, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.526744488331906, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.5286886658508809, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.5306400191947748, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.5325985748490616, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.5345643593969717, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.5365373995198517, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.538517721997528, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.5405053537086689, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.5425003216311501, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.5445026528424212, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [1]
检查交叉验证集中测试集标签是否有预测不到的值: {-1}
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 1.00 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.640 (+/-0.323) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.616 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.640 (+/-0.323) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.616 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.640 (+/-0.323) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 0.5248074602497725, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.526744488331906, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.5286886658508809, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.5306400191947748, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.5325985748490616, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.5345643593969717, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.5365373995198517, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.538517721997528, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.5405053537086689, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.5425003216311501, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.5445026528424212, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6400641025641025
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.672 (+/-0.392) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.698 (+/-0.383) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.681 (+/-0.372) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.681 (+/-0.372) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.662 (+/-0.389) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.681 (+/-0.372) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.662 (+/-0.389) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.626 (+/-0.318) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.698 (+/-0.383) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.635 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.626 (+/-0.318) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.772 (+/-0.473) for {'C': 0.5248074602497725, 'kernel': 'linear'}
0.772 (+/-0.473) for {'C': 0.526744488331906, 'kernel': 'linear'}
0.772 (+/-0.473) for {'C': 0.5286886658508809, 'kernel': 'linear'}
0.773 (+/-0.474) for {'C': 0.5306400191947748, 'kernel': 'linear'}
0.773 (+/-0.474) for {'C': 0.5325985748490616, 'kernel': 'linear'}
0.773 (+/-0.474) for {'C': 0.5345643593969717, 'kernel': 'linear'}
0.773 (+/-0.474) for {'C': 0.5365373995198517, 'kernel': 'linear'}
0.773 (+/-0.474) for {'C': 0.538517721997528, 'kernel': 'linear'}
0.773 (+/-0.474) for {'C': 0.5405053537086689, 'kernel': 'linear'}
0.773 (+/-0.474) for {'C': 0.5425003216311501, 'kernel': 'linear'}
0.773 (+/-0.474) for {'C': 0.5445026528424212, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.666 (+/-0.315) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.314) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.666 (+/-0.314) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.640 (+/-0.323) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.314) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.640 (+/-0.323) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.666 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.640 (+/-0.323) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.642 (+/-0.236) for {'C': 0.5248074602497725, 'kernel': 'linear'}
0.642 (+/-0.236) for {'C': 0.526744488331906, 'kernel': 'linear'}
0.642 (+/-0.236) for {'C': 0.5286886658508809, 'kernel': 'linear'}
0.667 (+/-0.316) for {'C': 0.5306400191947748, 'kernel': 'linear'}
0.667 (+/-0.316) for {'C': 0.5325985748490616, 'kernel': 'linear'}
0.667 (+/-0.316) for {'C': 0.5345643593969717, 'kernel': 'linear'}
0.667 (+/-0.316) for {'C': 0.5365373995198517, 'kernel': 'linear'}
0.667 (+/-0.316) for {'C': 0.538517721997528, 'kernel': 'linear'}
0.667 (+/-0.316) for {'C': 0.5405053537086689, 'kernel': 'linear'}
0.667 (+/-0.316) for {'C': 0.5425003216311501, 'kernel': 'linear'}
0.667 (+/-0.316) for {'C': 0.5445026528424212, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 0.5306400191947748, 'kernel': 'linear'}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6665064102564102
第2轮loop中,tunemodel函数得到的最优参数是: ([{'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['rbf'], 'gamma': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05])}, {'C': array([0.52868867, 0.52907836, 0.52946834, 0.52985862, 0.53024917,
0.53064002, 0.53103115, 0.53142258, 0.53181429, 0.53220629,
0.53259857]), 'kernel': ['linear']}], {'C': 0.5306400191947748, 'kernel': 'linear'}, 0.6665064102564102)
这是第2次迭代微调C和gamma。
第2次迭代,得到delta: [0.00392434]
SVC模型clf_op_iter的参数是: {'kernel': 'linear', 'decision_function_shape': 'ovr', 'class_weight': {-1: 30}, 'tol': 0.001, 'gamma': 'auto', 'random_state': 0, 'cache_size': 200, 'verbose': False, 'degree': 3, 'C': 0.5306400191947748, 'probability': False, 'shrinking': True, 'coef0': 0.0, 'max_iter': -1}
训练模型SVC对训练样本的预测准确率: 0.9029187817258884
测试集中,预测为舞弊样本的有: (array([ 370, 658, 769, 1246, 1247, 1248, 1251, 1252, 1255, 1256],
dtype=int64),)
测试集中,实际为舞弊样本的有: (array([1246, 1247, 1248, 1249, 1250, 1251, 1252, 1253, 1254, 1255, 1256],
dtype=int64),)
预测的舞弊样本数目: 10
训练模型SVC对测试样本的预测准确率: 0.9219543147208121
以上是第8次特征筛选。
第8次特征筛选,AUC值是: 0.8169779658543703
X_train_iter_svc.shape is: (1257, 44)
X_test_iter_svc.shape is: (1257, 44)
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.722 (+/-0.473) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.616 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6166666666666667
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.723 (+/-0.417) for {'C': 1.0, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.635 (+/-0.326) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.615 (+/-0.237) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6661858974358974
RBF的粗grid search最佳超参: {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0} RBF的粗grid search最高分值: 0.6166666666666667
linear的粗grid search最佳超参: {'C': 1.0, 'kernel': 'linear'} linear的粗grid search最高分值: 0.6661858974358974
粗grid search得到的parameter是:
[{'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['rbf'], 'gamma': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.662 (+/-0.389) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.662 (+/-0.389) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.626 (+/-0.318) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.635 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.626 (+/-0.318) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'linear'}
0.714 (+/-0.417) for {'C': 10.0, 'kernel': 'linear'}
0.639 (+/-0.326) for {'C': 100.0, 'kernel': 'linear'}
0.635 (+/-0.326) for {'C': 1000.0, 'kernel': 'linear'}
0.635 (+/-0.326) for {'C': 10000.0, 'kernel': 'linear'}
0.635 (+/-0.326) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [1]
检查交叉验证集中测试集标签是否有预测不到的值: {-1}
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 1.00 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.640 (+/-0.323) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.616 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.640 (+/-0.323) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.616 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.640 (+/-0.323) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'linear'}
0.665 (+/-0.315) for {'C': 10.0, 'kernel': 'linear'}
0.615 (+/-0.238) for {'C': 100.0, 'kernel': 'linear'}
0.615 (+/-0.237) for {'C': 1000.0, 'kernel': 'linear'}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'linear'}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.14 0.17 0.15 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 10.0, 'kernel': 'linear'}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6650641025641026
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.672 (+/-0.392) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.698 (+/-0.383) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.681 (+/-0.372) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.681 (+/-0.372) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.662 (+/-0.389) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.681 (+/-0.372) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.662 (+/-0.389) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.626 (+/-0.318) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.698 (+/-0.383) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.635 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.626 (+/-0.318) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.723 (+/-0.417) for {'C': 1.0, 'kernel': 'linear'}
0.679 (+/-0.373) for {'C': 10.0, 'kernel': 'linear'}
0.639 (+/-0.326) for {'C': 100.0, 'kernel': 'linear'}
0.635 (+/-0.326) for {'C': 1000.0, 'kernel': 'linear'}
0.635 (+/-0.326) for {'C': 10000.0, 'kernel': 'linear'}
0.635 (+/-0.326) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [1]
检查交叉验证集中测试集标签是否有预测不到的值: {-1}
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 1.00 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.666 (+/-0.315) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.314) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.666 (+/-0.314) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.640 (+/-0.323) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.314) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.640 (+/-0.323) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.666 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.640 (+/-0.323) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.664 (+/-0.314) for {'C': 10.0, 'kernel': 'linear'}
0.615 (+/-0.238) for {'C': 100.0, 'kernel': 'linear'}
0.615 (+/-0.237) for {'C': 1000.0, 'kernel': 'linear'}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'linear'}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6661858974358974
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.662 (+/-0.389) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.662 (+/-0.389) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.626 (+/-0.318) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.635 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.626 (+/-0.318) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.689 (+/-0.436) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.689 (+/-0.374) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.714 (+/-0.417) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [1]
检查交叉验证集中测试集标签是否有预测不到的值: {-1}
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 1.00 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.640 (+/-0.323) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.616 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.640 (+/-0.323) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.616 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.640 (+/-0.323) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.641 (+/-0.235) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.665 (+/-0.315) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.14 0.17 0.15 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 9.999999999999998, 'kernel': 'linear'}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6650641025641026
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.672 (+/-0.392) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.698 (+/-0.383) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.681 (+/-0.372) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.681 (+/-0.372) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.662 (+/-0.389) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.681 (+/-0.372) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.662 (+/-0.389) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.626 (+/-0.318) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.698 (+/-0.383) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.635 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.626 (+/-0.318) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.797 (+/-0.492) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.723 (+/-0.417) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.689 (+/-0.382) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.709 (+/-0.417) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.668 (+/-0.371) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.645 (+/-0.314) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.679 (+/-0.373) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.666 (+/-0.315) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.314) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.666 (+/-0.314) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.640 (+/-0.323) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.314) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.640 (+/-0.323) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.666 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.640 (+/-0.323) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.642 (+/-0.236) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.666 (+/-0.315) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.666 (+/-0.314) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.664 (+/-0.313) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.663 (+/-0.314) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.664 (+/-0.314) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 0.6309573444801931, 'kernel': 'linear'}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6663461538461538
循环迭代之前,delta is: [0.36904266]
执行tunemodel()函数前,使用的grid search parameter是:
[{'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['rbf'], 'gamma': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05])}, {'C': array([0.39810717, 0.43651583, 0.47863009, 0.52480746, 0.57543994,
0.63095734, 0.69183097, 0.75857758, 0.83176377, 0.91201084,
1. ]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.662 (+/-0.389) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.662 (+/-0.389) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.626 (+/-0.318) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.635 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.626 (+/-0.318) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.43651583224016594, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.47863009232263826, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.5248074602497725, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.5754399373371568, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.6918309709189364, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.7585775750291837, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.8317637711026709, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.9120108393559095, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.9999999999999997, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [1]
检查交叉验证集中测试集标签是否有预测不到的值: {-1}
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 1.00 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.640 (+/-0.323) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.616 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.640 (+/-0.323) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.616 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.640 (+/-0.323) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.43651583224016594, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.47863009232263826, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.5248074602497725, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.5754399373371568, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.6918309709189364, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.7585775750291837, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.8317637711026709, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.9120108393559095, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.9999999999999997, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6399038461538461
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.672 (+/-0.392) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.698 (+/-0.383) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.681 (+/-0.372) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.681 (+/-0.372) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.662 (+/-0.389) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.681 (+/-0.372) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.662 (+/-0.389) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.626 (+/-0.318) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.698 (+/-0.383) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.635 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.626 (+/-0.318) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.797 (+/-0.492) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.797 (+/-0.492) for {'C': 0.43651583224016594, 'kernel': 'linear'}
0.797 (+/-0.492) for {'C': 0.47863009232263826, 'kernel': 'linear'}
0.772 (+/-0.473) for {'C': 0.5248074602497725, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.5754399373371568, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.6918309709189364, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.7585775750291837, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.8317637711026709, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.9120108393559095, 'kernel': 'linear'}
0.723 (+/-0.417) for {'C': 0.9999999999999997, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.666 (+/-0.315) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.314) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.666 (+/-0.314) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.640 (+/-0.323) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.314) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.640 (+/-0.323) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.666 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.640 (+/-0.323) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.642 (+/-0.236) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.642 (+/-0.236) for {'C': 0.43651583224016594, 'kernel': 'linear'}
0.642 (+/-0.236) for {'C': 0.47863009232263826, 'kernel': 'linear'}
0.642 (+/-0.236) for {'C': 0.5248074602497725, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.5754399373371568, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.6918309709189364, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.7585775750291837, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.8317637711026709, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.9120108393559095, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.9999999999999997, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 0.5754399373371568, 'kernel': 'linear'}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6663461538461538
第0轮loop中,tunemodel函数得到的最优参数是: ([{'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['rbf'], 'gamma': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05])}, {'C': array([0.52480746, 0.53456436, 0.54450265, 0.55462571, 0.56493697,
0.57543994, 0.58613816, 0.59703529, 0.608135 , 0.61944108,
0.63095734]), 'kernel': ['linear']}], {'C': 0.5754399373371568, 'kernel': 'linear'}, 0.6663461538461538)
这是第0次迭代微调C和gamma。
第0次迭代,得到delta: [0.05551741]
执行tunemodel()函数前,使用的grid search parameter是:
[{'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['rbf'], 'gamma': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05])}, {'C': array([0.52480746, 0.53456436, 0.54450265, 0.55462571, 0.56493697,
0.57543994, 0.58613816, 0.59703529, 0.608135 , 0.61944108,
0.63095734]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.662 (+/-0.389) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.662 (+/-0.389) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.626 (+/-0.318) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.635 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.626 (+/-0.318) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 0.5248074602497725, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.5345643593969717, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.5445026528424212, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.5546257129579107, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.5649369748123024, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.5754399373371568, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.5861381645140287, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.5970352865838368, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.6081350012787178, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.6194410750767814, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.6309573444801931, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [1]
检查交叉验证集中测试集标签是否有预测不到的值: {-1}
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 1.00 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.640 (+/-0.323) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.616 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.640 (+/-0.323) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.616 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.640 (+/-0.323) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 0.5248074602497725, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.5345643593969717, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.5445026528424212, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.5546257129579107, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.5649369748123024, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.5754399373371568, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.5861381645140287, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.5970352865838368, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.6081350012787178, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.6194410750767814, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.6309573444801931, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6399038461538461
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.672 (+/-0.392) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.698 (+/-0.383) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.681 (+/-0.372) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.681 (+/-0.372) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.662 (+/-0.389) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.681 (+/-0.372) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.662 (+/-0.389) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.626 (+/-0.318) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.698 (+/-0.383) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.635 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.626 (+/-0.318) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.772 (+/-0.473) for {'C': 0.5248074602497725, 'kernel': 'linear'}
0.772 (+/-0.473) for {'C': 0.5345643593969717, 'kernel': 'linear'}
0.773 (+/-0.474) for {'C': 0.5445026528424212, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.5546257129579107, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.5649369748123024, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.5754399373371568, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.5861381645140287, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.5970352865838368, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.6081350012787178, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.6194410750767814, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.6309573444801931, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.666 (+/-0.315) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.314) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.666 (+/-0.314) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.640 (+/-0.323) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.314) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.640 (+/-0.323) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.666 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.640 (+/-0.323) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.642 (+/-0.236) for {'C': 0.5248074602497725, 'kernel': 'linear'}
0.642 (+/-0.236) for {'C': 0.5345643593969717, 'kernel': 'linear'}
0.667 (+/-0.316) for {'C': 0.5445026528424212, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.5546257129579107, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.5649369748123024, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.5754399373371568, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.5861381645140287, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.5970352865838368, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.6081350012787178, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.6194410750767814, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.6309573444801931, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 0.5445026528424212, 'kernel': 'linear'}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6665064102564102
第1轮loop中,tunemodel函数得到的最优参数是: ([{'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['rbf'], 'gamma': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05])}, {'C': array([0.53456436, 0.5365374 , 0.53851772, 0.54050535, 0.54250032,
0.54450265, 0.54651237, 0.54852951, 0.5505541 , 0.55258616,
0.55462571]), 'kernel': ['linear']}], {'C': 0.5445026528424212, 'kernel': 'linear'}, 0.6665064102564102)
这是第1次迭代微调C和gamma。
第1次迭代,得到delta: [0.03093728]
执行tunemodel()函数前,使用的grid search parameter是:
[{'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['rbf'], 'gamma': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05])}, {'C': array([0.53456436, 0.5365374 , 0.53851772, 0.54050535, 0.54250032,
0.54450265, 0.54651237, 0.54852951, 0.5505541 , 0.55258616,
0.55462571]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.662 (+/-0.389) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.662 (+/-0.389) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.626 (+/-0.318) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.635 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.626 (+/-0.318) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 0.5345643593969717, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.5365373995198518, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.538517721997528, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.5405053537086689, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.5425003216311503, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.5445026528424212, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.5465123745198721, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.5485295139412031, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.5505540984847945, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.5525861556300782, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.5546257129579107, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [1]
检查交叉验证集中测试集标签是否有预测不到的值: {-1}
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 1.00 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.640 (+/-0.323) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.616 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.640 (+/-0.323) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.616 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.640 (+/-0.323) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 0.5345643593969717, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.5365373995198518, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.538517721997528, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.5405053537086689, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.5425003216311503, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.5445026528424212, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.5465123745198721, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.5485295139412031, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.5505540984847945, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.5525861556300782, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.5546257129579107, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6399038461538461
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.672 (+/-0.392) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.698 (+/-0.383) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.681 (+/-0.372) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.681 (+/-0.372) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.662 (+/-0.389) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.681 (+/-0.372) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.662 (+/-0.389) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.626 (+/-0.318) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.698 (+/-0.383) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.635 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.626 (+/-0.318) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.772 (+/-0.473) for {'C': 0.5345643593969717, 'kernel': 'linear'}
0.773 (+/-0.474) for {'C': 0.5365373995198518, 'kernel': 'linear'}
0.773 (+/-0.474) for {'C': 0.538517721997528, 'kernel': 'linear'}
0.773 (+/-0.474) for {'C': 0.5405053537086689, 'kernel': 'linear'}
0.773 (+/-0.474) for {'C': 0.5425003216311503, 'kernel': 'linear'}
0.773 (+/-0.474) for {'C': 0.5445026528424212, 'kernel': 'linear'}
0.773 (+/-0.474) for {'C': 0.5465123745198721, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.5485295139412031, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.5505540984847945, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.5525861556300782, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.5546257129579107, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.666 (+/-0.315) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.314) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.666 (+/-0.314) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.640 (+/-0.323) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.314) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.640 (+/-0.323) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.666 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.640 (+/-0.323) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.642 (+/-0.236) for {'C': 0.5345643593969717, 'kernel': 'linear'}
0.667 (+/-0.316) for {'C': 0.5365373995198518, 'kernel': 'linear'}
0.667 (+/-0.316) for {'C': 0.538517721997528, 'kernel': 'linear'}
0.667 (+/-0.316) for {'C': 0.5405053537086689, 'kernel': 'linear'}
0.667 (+/-0.316) for {'C': 0.5425003216311503, 'kernel': 'linear'}
0.667 (+/-0.316) for {'C': 0.5445026528424212, 'kernel': 'linear'}
0.667 (+/-0.316) for {'C': 0.5465123745198721, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.5485295139412031, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.5505540984847945, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.5525861556300782, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.5546257129579107, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 0.5365373995198518, 'kernel': 'linear'}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6665064102564102
第2轮loop中,tunemodel函数得到的最优参数是: ([{'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['rbf'], 'gamma': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05])}, {'C': array([0.53456436, 0.53495839, 0.5353527 , 0.53574731, 0.53614221,
0.5365374 , 0.53693288, 0.53732865, 0.53772472, 0.53812107,
0.53851772]), 'kernel': ['linear']}], {'C': 0.5365373995198518, 'kernel': 'linear'}, 0.6665064102564102)
这是第2次迭代微调C和gamma。
第2次迭代,得到delta: [0.00796525]
SVC模型clf_op_iter的参数是: {'kernel': 'linear', 'decision_function_shape': 'ovr', 'class_weight': {-1: 30}, 'tol': 0.001, 'gamma': 'auto', 'random_state': 0, 'cache_size': 200, 'verbose': False, 'degree': 3, 'C': 0.5365373995198518, 'probability': False, 'shrinking': True, 'coef0': 0.0, 'max_iter': -1}
训练模型SVC对训练样本的预测准确率: 0.9029187817258884
测试集中,预测为舞弊样本的有: (array([ 370, 658, 769, 1246, 1247, 1248, 1251, 1252, 1255, 1256],
dtype=int64),)
测试集中,实际为舞弊样本的有: (array([1246, 1247, 1248, 1249, 1250, 1251, 1252, 1253, 1254, 1255, 1256],
dtype=int64),)
预测的舞弊样本数目: 10
训练模型SVC对测试样本的预测准确率: 0.9219543147208121
以上是第9次特征筛选。
第9次特征筛选,AUC值是: 0.8169779658543703
X_train_iter_svc.shape is: (1257, 43)
X_test_iter_svc.shape is: (1257, 43)
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.664 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.615 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6166666666666667
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.723 (+/-0.417) for {'C': 1.0, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.656 (+/-0.384) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.614 (+/-0.240) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6661858974358974
RBF的粗grid search最佳超参: {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0} RBF的粗grid search最高分值: 0.6166666666666667
linear的粗grid search最佳超参: {'C': 1.0, 'kernel': 'linear'} linear的粗grid search最高分值: 0.6661858974358974
粗grid search得到的parameter是:
[{'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['rbf'], 'gamma': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.639 (+/-0.396) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.664 (+/-0.374) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.668 (+/-0.378) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.656 (+/-0.384) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.668 (+/-0.378) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.656 (+/-0.384) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.656 (+/-0.384) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'linear'}
0.689 (+/-0.436) for {'C': 10.0, 'kernel': 'linear'}
0.666 (+/-0.372) for {'C': 100.0, 'kernel': 'linear'}
0.656 (+/-0.384) for {'C': 1000.0, 'kernel': 'linear'}
0.656 (+/-0.384) for {'C': 10000.0, 'kernel': 'linear'}
0.656 (+/-0.384) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [1]
检查交叉验证集中测试集标签是否有预测不到的值: {-1}
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 1.00 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.588 (+/-0.232) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.639 (+/-0.236) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.616 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.639 (+/-0.236) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.240) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.616 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.639 (+/-0.236) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.614 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'linear'}
0.616 (+/-0.238) for {'C': 10.0, 'kernel': 'linear'}
0.638 (+/-0.238) for {'C': 100.0, 'kernel': 'linear'}
0.614 (+/-0.240) for {'C': 1000.0, 'kernel': 'linear'}
0.614 (+/-0.240) for {'C': 10000.0, 'kernel': 'linear'}
0.614 (+/-0.240) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.14 0.17 0.15 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6389423076923078
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.664 (+/-0.388) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.383) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.639 (+/-0.396) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.698 (+/-0.383) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.655 (+/-0.378) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.698 (+/-0.383) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.661 (+/-0.384) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.656 (+/-0.384) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.698 (+/-0.383) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.663 (+/-0.382) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.656 (+/-0.384) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.656 (+/-0.384) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.723 (+/-0.417) for {'C': 1.0, 'kernel': 'linear'}
0.653 (+/-0.379) for {'C': 10.0, 'kernel': 'linear'}
0.666 (+/-0.372) for {'C': 100.0, 'kernel': 'linear'}
0.656 (+/-0.384) for {'C': 1000.0, 'kernel': 'linear'}
0.656 (+/-0.384) for {'C': 10000.0, 'kernel': 'linear'}
0.656 (+/-0.384) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [1]
检查交叉验证集中测试集标签是否有预测不到的值: {-1}
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 1.00 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.315) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.588 (+/-0.232) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.666 (+/-0.315) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.637 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.315) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.637 (+/-0.236) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.240) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.666 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.638 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.614 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.661 (+/-0.313) for {'C': 10.0, 'kernel': 'linear'}
0.638 (+/-0.238) for {'C': 100.0, 'kernel': 'linear'}
0.614 (+/-0.240) for {'C': 1000.0, 'kernel': 'linear'}
0.614 (+/-0.240) for {'C': 10000.0, 'kernel': 'linear'}
0.614 (+/-0.240) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6661858974358974
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.639 (+/-0.396) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.664 (+/-0.374) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.668 (+/-0.378) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.656 (+/-0.384) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.668 (+/-0.378) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.656 (+/-0.384) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.656 (+/-0.384) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.664 (+/-0.388) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.689 (+/-0.436) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.689 (+/-0.436) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [1]
检查交叉验证集中测试集标签是否有预测不到的值: {-1}
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 1.00 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.588 (+/-0.232) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.639 (+/-0.236) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.616 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.639 (+/-0.236) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.240) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.616 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.639 (+/-0.236) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.614 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.616 (+/-0.238) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.14 0.17 0.15 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6389423076923078
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.664 (+/-0.388) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.383) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.639 (+/-0.396) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.698 (+/-0.383) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.655 (+/-0.378) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.698 (+/-0.383) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.661 (+/-0.384) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.656 (+/-0.384) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.698 (+/-0.383) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.663 (+/-0.382) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.656 (+/-0.384) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.656 (+/-0.384) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.747 (+/-0.449) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.723 (+/-0.417) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.689 (+/-0.374) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.698 (+/-0.383) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.681 (+/-0.372) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.659 (+/-0.375) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.653 (+/-0.379) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.315) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.588 (+/-0.232) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.666 (+/-0.315) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.637 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.315) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.637 (+/-0.236) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.240) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.666 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.638 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.614 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.641 (+/-0.236) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.666 (+/-0.314) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.666 (+/-0.315) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.665 (+/-0.314) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.663 (+/-0.313) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.661 (+/-0.313) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 0.9999999999999997, 'kernel': 'linear'}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6661858974358974
循环迭代之前,delta is: [3.33066907e-16]
SVC模型clf_op_iter的参数是: {'kernel': 'linear', 'decision_function_shape': 'ovr', 'class_weight': {-1: 30}, 'tol': 0.001, 'gamma': 'auto', 'random_state': 0, 'cache_size': 200, 'verbose': False, 'degree': 3, 'C': 0.9999999999999997, 'probability': False, 'shrinking': True, 'coef0': 0.0, 'max_iter': -1}
训练模型SVC对训练样本的预测准确率: 0.9016497461928934
测试集中,预测为舞弊样本的有: (array([ 370, 1248, 1251, 1252, 1255, 1256], dtype=int64),)
测试集中,实际为舞弊样本的有: (array([1246, 1247, 1248, 1249, 1250, 1251, 1252, 1253, 1254, 1255, 1256],
dtype=int64),)
预测的舞弊样本数目: 6
训练模型SVC对测试样本的预测准确率: 0.8851522842639594
以上是第10次特征筛选。
第10次特征筛选,AUC值是: 0.726871443163578
X_train_iter_svc.shape is: (1257, 42)
X_test_iter_svc.shape is: (1257, 42)
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.664 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.615 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6166666666666667
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.723 (+/-0.417) for {'C': 1.0, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.656 (+/-0.384) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.614 (+/-0.240) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6661858974358974
RBF的粗grid search最佳超参: {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0} RBF的粗grid search最高分值: 0.6166666666666667
linear的粗grid search最佳超参: {'C': 1.0, 'kernel': 'linear'} linear的粗grid search最高分值: 0.6661858974358974
粗grid search得到的parameter是:
[{'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['rbf'], 'gamma': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.639 (+/-0.396) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.664 (+/-0.374) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.668 (+/-0.378) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.656 (+/-0.384) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.668 (+/-0.378) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.656 (+/-0.384) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.656 (+/-0.384) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'linear'}
0.689 (+/-0.436) for {'C': 10.0, 'kernel': 'linear'}
0.656 (+/-0.384) for {'C': 100.0, 'kernel': 'linear'}
0.656 (+/-0.384) for {'C': 1000.0, 'kernel': 'linear'}
0.656 (+/-0.384) for {'C': 10000.0, 'kernel': 'linear'}
0.656 (+/-0.384) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [1]
检查交叉验证集中测试集标签是否有预测不到的值: {-1}
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 1.00 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.588 (+/-0.232) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.639 (+/-0.236) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.616 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.639 (+/-0.236) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.240) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.616 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.639 (+/-0.236) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.614 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'linear'}
0.616 (+/-0.238) for {'C': 10.0, 'kernel': 'linear'}
0.613 (+/-0.241) for {'C': 100.0, 'kernel': 'linear'}
0.614 (+/-0.240) for {'C': 1000.0, 'kernel': 'linear'}
0.614 (+/-0.240) for {'C': 10000.0, 'kernel': 'linear'}
0.614 (+/-0.240) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.14 0.17 0.15 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6389423076923078
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.664 (+/-0.388) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.383) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.639 (+/-0.396) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.698 (+/-0.383) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.655 (+/-0.378) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.698 (+/-0.383) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.662 (+/-0.383) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.656 (+/-0.384) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.698 (+/-0.383) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.663 (+/-0.382) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.656 (+/-0.384) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.656 (+/-0.384) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.723 (+/-0.417) for {'C': 1.0, 'kernel': 'linear'}
0.653 (+/-0.379) for {'C': 10.0, 'kernel': 'linear'}
0.656 (+/-0.384) for {'C': 100.0, 'kernel': 'linear'}
0.656 (+/-0.384) for {'C': 1000.0, 'kernel': 'linear'}
0.656 (+/-0.384) for {'C': 10000.0, 'kernel': 'linear'}
0.656 (+/-0.384) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [1]
检查交叉验证集中测试集标签是否有预测不到的值: {-1}
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 1.00 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.315) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.588 (+/-0.232) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.666 (+/-0.315) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.637 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.315) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.637 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.240) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.666 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.638 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.614 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.661 (+/-0.313) for {'C': 10.0, 'kernel': 'linear'}
0.613 (+/-0.241) for {'C': 100.0, 'kernel': 'linear'}
0.614 (+/-0.240) for {'C': 1000.0, 'kernel': 'linear'}
0.614 (+/-0.240) for {'C': 10000.0, 'kernel': 'linear'}
0.614 (+/-0.240) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6661858974358974
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.639 (+/-0.396) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.664 (+/-0.374) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.668 (+/-0.378) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.656 (+/-0.384) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.668 (+/-0.378) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.656 (+/-0.384) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.656 (+/-0.384) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.664 (+/-0.388) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.689 (+/-0.436) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.689 (+/-0.436) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [1]
检查交叉验证集中测试集标签是否有预测不到的值: {-1}
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 1.00 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.588 (+/-0.232) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.639 (+/-0.236) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.616 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.639 (+/-0.236) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.240) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.616 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.639 (+/-0.236) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.614 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.616 (+/-0.238) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.14 0.17 0.15 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6389423076923078
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.664 (+/-0.388) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.383) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.639 (+/-0.396) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.698 (+/-0.383) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.655 (+/-0.378) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.698 (+/-0.383) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.662 (+/-0.383) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.656 (+/-0.384) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.698 (+/-0.383) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.663 (+/-0.382) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.656 (+/-0.384) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.656 (+/-0.384) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.747 (+/-0.449) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.723 (+/-0.417) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.689 (+/-0.374) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.698 (+/-0.383) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.681 (+/-0.372) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.659 (+/-0.375) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.653 (+/-0.379) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.315) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.588 (+/-0.232) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.666 (+/-0.315) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.637 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.315) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.637 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.240) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.666 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.638 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.614 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.641 (+/-0.236) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.666 (+/-0.314) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.666 (+/-0.315) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.665 (+/-0.314) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.663 (+/-0.313) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.661 (+/-0.313) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 0.9999999999999997, 'kernel': 'linear'}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6661858974358974
循环迭代之前,delta is: [3.33066907e-16]
SVC模型clf_op_iter的参数是: {'kernel': 'linear', 'decision_function_shape': 'ovr', 'class_weight': {-1: 30}, 'tol': 0.001, 'gamma': 'auto', 'random_state': 0, 'cache_size': 200, 'verbose': False, 'degree': 3, 'C': 0.9999999999999997, 'probability': False, 'shrinking': True, 'coef0': 0.0, 'max_iter': -1}
训练模型SVC对训练样本的预测准确率: 0.9016497461928934
测试集中,预测为舞弊样本的有: (array([ 370, 1248, 1251, 1252, 1255, 1256], dtype=int64),)
测试集中,实际为舞弊样本的有: (array([1246, 1247, 1248, 1249, 1250, 1251, 1252, 1253, 1254, 1255, 1256],
dtype=int64),)
预测的舞弊样本数目: 6
训练模型SVC对测试样本的预测准确率: 0.8851522842639594
以上是第11次特征筛选。
第11次特征筛选,AUC值是: 0.726871443163578
X_train_iter_svc.shape is: (1257, 41)
X_test_iter_svc.shape is: (1257, 41)
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.664 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.615 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6166666666666667
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.723 (+/-0.417) for {'C': 1.0, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.656 (+/-0.384) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.614 (+/-0.240) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6661858974358974
RBF的粗grid search最佳超参: {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0} RBF的粗grid search最高分值: 0.6166666666666667
linear的粗grid search最佳超参: {'C': 1.0, 'kernel': 'linear'} linear的粗grid search最高分值: 0.6661858974358974
粗grid search得到的parameter是:
[{'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['rbf'], 'gamma': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.639 (+/-0.396) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.664 (+/-0.374) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.668 (+/-0.378) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.656 (+/-0.384) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.668 (+/-0.378) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.656 (+/-0.384) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.656 (+/-0.384) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'linear'}
0.689 (+/-0.436) for {'C': 10.0, 'kernel': 'linear'}
0.656 (+/-0.384) for {'C': 100.0, 'kernel': 'linear'}
0.656 (+/-0.384) for {'C': 1000.0, 'kernel': 'linear'}
0.656 (+/-0.384) for {'C': 10000.0, 'kernel': 'linear'}
0.656 (+/-0.384) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [1]
检查交叉验证集中测试集标签是否有预测不到的值: {-1}
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 1.00 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.588 (+/-0.232) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.639 (+/-0.236) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.616 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.639 (+/-0.236) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.240) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.616 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.639 (+/-0.236) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.614 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'linear'}
0.616 (+/-0.238) for {'C': 10.0, 'kernel': 'linear'}
0.613 (+/-0.241) for {'C': 100.0, 'kernel': 'linear'}
0.614 (+/-0.240) for {'C': 1000.0, 'kernel': 'linear'}
0.614 (+/-0.240) for {'C': 10000.0, 'kernel': 'linear'}
0.614 (+/-0.240) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.14 0.17 0.15 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6389423076923078
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.664 (+/-0.388) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.383) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.639 (+/-0.396) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.698 (+/-0.383) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.655 (+/-0.378) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.698 (+/-0.383) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.662 (+/-0.383) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.656 (+/-0.384) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.698 (+/-0.383) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.663 (+/-0.382) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.656 (+/-0.384) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.656 (+/-0.384) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.723 (+/-0.417) for {'C': 1.0, 'kernel': 'linear'}
0.653 (+/-0.379) for {'C': 10.0, 'kernel': 'linear'}
0.656 (+/-0.384) for {'C': 100.0, 'kernel': 'linear'}
0.656 (+/-0.384) for {'C': 1000.0, 'kernel': 'linear'}
0.656 (+/-0.384) for {'C': 10000.0, 'kernel': 'linear'}
0.656 (+/-0.384) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [1]
检查交叉验证集中测试集标签是否有预测不到的值: {-1}
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 1.00 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.315) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.588 (+/-0.232) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.666 (+/-0.315) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.637 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.315) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.637 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.240) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.666 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.638 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.614 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.661 (+/-0.313) for {'C': 10.0, 'kernel': 'linear'}
0.613 (+/-0.241) for {'C': 100.0, 'kernel': 'linear'}
0.614 (+/-0.240) for {'C': 1000.0, 'kernel': 'linear'}
0.614 (+/-0.240) for {'C': 10000.0, 'kernel': 'linear'}
0.614 (+/-0.240) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6661858974358974
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.639 (+/-0.396) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.664 (+/-0.374) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.668 (+/-0.378) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.656 (+/-0.384) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.668 (+/-0.378) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.656 (+/-0.384) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.656 (+/-0.384) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.664 (+/-0.388) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.689 (+/-0.436) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.689 (+/-0.436) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [1]
检查交叉验证集中测试集标签是否有预测不到的值: {-1}
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 1.00 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.588 (+/-0.232) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.639 (+/-0.236) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.616 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.639 (+/-0.236) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.240) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.616 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.639 (+/-0.236) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.614 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.616 (+/-0.238) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.14 0.17 0.15 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6389423076923078
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.664 (+/-0.388) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.383) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.639 (+/-0.396) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.698 (+/-0.383) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.655 (+/-0.378) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.698 (+/-0.383) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.662 (+/-0.383) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.656 (+/-0.384) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.698 (+/-0.383) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.663 (+/-0.382) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.656 (+/-0.384) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.656 (+/-0.384) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.747 (+/-0.449) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.723 (+/-0.417) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.689 (+/-0.374) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.698 (+/-0.383) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.681 (+/-0.372) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.659 (+/-0.375) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.653 (+/-0.379) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.315) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.588 (+/-0.232) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.666 (+/-0.315) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.637 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.315) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.637 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.240) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.666 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.638 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.614 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.641 (+/-0.236) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.666 (+/-0.314) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.666 (+/-0.315) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.665 (+/-0.314) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.663 (+/-0.313) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.661 (+/-0.313) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 0.9999999999999997, 'kernel': 'linear'}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6661858974358974
循环迭代之前,delta is: [3.33066907e-16]
SVC模型clf_op_iter的参数是: {'kernel': 'linear', 'decision_function_shape': 'ovr', 'class_weight': {-1: 30}, 'tol': 0.001, 'gamma': 'auto', 'random_state': 0, 'cache_size': 200, 'verbose': False, 'degree': 3, 'C': 0.9999999999999997, 'probability': False, 'shrinking': True, 'coef0': 0.0, 'max_iter': -1}
训练模型SVC对训练样本的预测准确率: 0.9016497461928934
测试集中,预测为舞弊样本的有: (array([ 370, 1248, 1251, 1252, 1255, 1256], dtype=int64),)
测试集中,实际为舞弊样本的有: (array([1246, 1247, 1248, 1249, 1250, 1251, 1252, 1253, 1254, 1255, 1256],
dtype=int64),)
预测的舞弊样本数目: 6
训练模型SVC对测试样本的预测准确率: 0.8851522842639594
以上是第12次特征筛选。
第12次特征筛选,AUC值是: 0.726871443163578
X_train_iter_svc.shape is: (1257, 40)
X_test_iter_svc.shape is: (1257, 40)
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.639 (+/-0.326) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.615 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6166666666666667
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.723 (+/-0.417) for {'C': 1.0, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.664 (+/-0.388) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.614 (+/-0.240) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6661858974358974
RBF的粗grid search最佳超参: {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0} RBF的粗grid search最高分值: 0.6166666666666667
linear的粗grid search最佳超参: {'C': 1.0, 'kernel': 'linear'} linear的粗grid search最高分值: 0.6661858974358974
粗grid search得到的parameter是:
[{'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['rbf'], 'gamma': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.639 (+/-0.396) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.326) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.664 (+/-0.374) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.326) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.668 (+/-0.378) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.656 (+/-0.384) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.326) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.668 (+/-0.378) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.656 (+/-0.384) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'linear'}
0.689 (+/-0.436) for {'C': 10.0, 'kernel': 'linear'}
0.656 (+/-0.384) for {'C': 100.0, 'kernel': 'linear'}
0.664 (+/-0.388) for {'C': 1000.0, 'kernel': 'linear'}
0.664 (+/-0.388) for {'C': 10000.0, 'kernel': 'linear'}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [1]
检查交叉验证集中测试集标签是否有预测不到的值: {-1}
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 1.00 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.589 (+/-0.232) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.639 (+/-0.236) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.616 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.639 (+/-0.236) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.240) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.616 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.639 (+/-0.236) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.614 (+/-0.241) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'linear'}
0.616 (+/-0.238) for {'C': 10.0, 'kernel': 'linear'}
0.613 (+/-0.241) for {'C': 100.0, 'kernel': 'linear'}
0.614 (+/-0.240) for {'C': 1000.0, 'kernel': 'linear'}
0.614 (+/-0.240) for {'C': 10000.0, 'kernel': 'linear'}
0.614 (+/-0.240) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.14 0.17 0.15 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6389423076923078
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.664 (+/-0.388) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.383) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.639 (+/-0.396) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.326) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.698 (+/-0.383) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.655 (+/-0.378) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.326) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.698 (+/-0.383) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.662 (+/-0.383) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.656 (+/-0.384) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.326) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.698 (+/-0.383) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.379) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.656 (+/-0.384) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.723 (+/-0.417) for {'C': 1.0, 'kernel': 'linear'}
0.653 (+/-0.379) for {'C': 10.0, 'kernel': 'linear'}
0.656 (+/-0.384) for {'C': 100.0, 'kernel': 'linear'}
0.664 (+/-0.388) for {'C': 1000.0, 'kernel': 'linear'}
0.664 (+/-0.388) for {'C': 10000.0, 'kernel': 'linear'}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [1]
检查交叉验证集中测试集标签是否有预测不到的值: {-1}
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 1.00 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.315) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.588 (+/-0.232) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.666 (+/-0.315) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.637 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.315) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.637 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.240) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.666 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.662 (+/-0.314) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.614 (+/-0.241) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.661 (+/-0.314) for {'C': 10.0, 'kernel': 'linear'}
0.613 (+/-0.241) for {'C': 100.0, 'kernel': 'linear'}
0.614 (+/-0.240) for {'C': 1000.0, 'kernel': 'linear'}
0.614 (+/-0.240) for {'C': 10000.0, 'kernel': 'linear'}
0.614 (+/-0.240) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6661858974358974
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.639 (+/-0.396) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.326) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.664 (+/-0.374) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.326) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.668 (+/-0.378) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.656 (+/-0.384) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.326) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.668 (+/-0.378) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.656 (+/-0.384) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.664 (+/-0.388) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.689 (+/-0.436) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.689 (+/-0.436) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [1]
检查交叉验证集中测试集标签是否有预测不到的值: {-1}
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 1.00 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.589 (+/-0.232) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.639 (+/-0.236) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.616 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.639 (+/-0.236) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.240) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.616 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.639 (+/-0.236) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.614 (+/-0.241) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.616 (+/-0.238) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.14 0.17 0.15 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6389423076923078
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.664 (+/-0.388) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.383) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.639 (+/-0.396) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.326) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.698 (+/-0.383) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.655 (+/-0.378) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.326) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.698 (+/-0.383) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.662 (+/-0.383) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.656 (+/-0.384) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.326) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.698 (+/-0.383) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.379) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.656 (+/-0.384) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.747 (+/-0.449) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.723 (+/-0.417) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.689 (+/-0.374) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.698 (+/-0.383) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.681 (+/-0.372) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.659 (+/-0.375) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.653 (+/-0.379) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.315) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.588 (+/-0.232) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.666 (+/-0.315) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.637 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.315) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.637 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.240) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.666 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.662 (+/-0.314) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.614 (+/-0.241) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.641 (+/-0.236) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.666 (+/-0.314) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.666 (+/-0.315) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.665 (+/-0.314) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.663 (+/-0.313) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.661 (+/-0.314) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 0.9999999999999997, 'kernel': 'linear'}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6661858974358974
循环迭代之前,delta is: [3.33066907e-16]
SVC模型clf_op_iter的参数是: {'kernel': 'linear', 'decision_function_shape': 'ovr', 'class_weight': {-1: 30}, 'tol': 0.001, 'gamma': 'auto', 'random_state': 0, 'cache_size': 200, 'verbose': False, 'degree': 3, 'C': 0.9999999999999997, 'probability': False, 'shrinking': True, 'coef0': 0.0, 'max_iter': -1}
训练模型SVC对训练样本的预测准确率: 0.9016497461928934
测试集中,预测为舞弊样本的有: (array([ 370, 1248, 1251, 1252, 1255, 1256], dtype=int64),)
测试集中,实际为舞弊样本的有: (array([1246, 1247, 1248, 1249, 1250, 1251, 1252, 1253, 1254, 1255, 1256],
dtype=int64),)
预测的舞弊样本数目: 6
训练模型SVC对测试样本的预测准确率: 0.8851522842639594
以上是第13次特征筛选。
第13次特征筛选,AUC值是: 0.726871443163578
X_train_iter_svc.shape is: (1257, 39)
X_test_iter_svc.shape is: (1257, 39)
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.639 (+/-0.326) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.615 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6166666666666667
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.723 (+/-0.417) for {'C': 1.0, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.664 (+/-0.388) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.614 (+/-0.240) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6661858974358974
RBF的粗grid search最佳超参: {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0} RBF的粗grid search最高分值: 0.6166666666666667
linear的粗grid search最佳超参: {'C': 1.0, 'kernel': 'linear'} linear的粗grid search最高分值: 0.6661858974358974
粗grid search得到的parameter是:
[{'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['rbf'], 'gamma': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.639 (+/-0.396) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.326) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.664 (+/-0.374) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.326) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.668 (+/-0.378) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.656 (+/-0.384) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.326) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.668 (+/-0.378) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.656 (+/-0.384) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'linear'}
0.689 (+/-0.436) for {'C': 10.0, 'kernel': 'linear'}
0.656 (+/-0.384) for {'C': 100.0, 'kernel': 'linear'}
0.664 (+/-0.388) for {'C': 1000.0, 'kernel': 'linear'}
0.664 (+/-0.388) for {'C': 10000.0, 'kernel': 'linear'}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [1]
检查交叉验证集中测试集标签是否有预测不到的值: {-1}
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 1.00 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.589 (+/-0.232) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.639 (+/-0.236) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.616 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.639 (+/-0.236) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.240) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.616 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.639 (+/-0.236) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.614 (+/-0.241) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'linear'}
0.616 (+/-0.238) for {'C': 10.0, 'kernel': 'linear'}
0.613 (+/-0.241) for {'C': 100.0, 'kernel': 'linear'}
0.614 (+/-0.240) for {'C': 1000.0, 'kernel': 'linear'}
0.614 (+/-0.240) for {'C': 10000.0, 'kernel': 'linear'}
0.614 (+/-0.240) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.14 0.17 0.15 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6389423076923078
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.664 (+/-0.388) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.383) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.639 (+/-0.396) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.326) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.698 (+/-0.383) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.655 (+/-0.378) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.326) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.698 (+/-0.383) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.662 (+/-0.383) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.656 (+/-0.384) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.326) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.698 (+/-0.383) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.379) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.656 (+/-0.384) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.723 (+/-0.417) for {'C': 1.0, 'kernel': 'linear'}
0.653 (+/-0.379) for {'C': 10.0, 'kernel': 'linear'}
0.656 (+/-0.384) for {'C': 100.0, 'kernel': 'linear'}
0.664 (+/-0.388) for {'C': 1000.0, 'kernel': 'linear'}
0.664 (+/-0.388) for {'C': 10000.0, 'kernel': 'linear'}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [1]
检查交叉验证集中测试集标签是否有预测不到的值: {-1}
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 1.00 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.315) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.588 (+/-0.232) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.666 (+/-0.315) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.637 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.315) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.637 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.240) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.666 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.662 (+/-0.314) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.614 (+/-0.241) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.661 (+/-0.314) for {'C': 10.0, 'kernel': 'linear'}
0.613 (+/-0.241) for {'C': 100.0, 'kernel': 'linear'}
0.614 (+/-0.240) for {'C': 1000.0, 'kernel': 'linear'}
0.614 (+/-0.240) for {'C': 10000.0, 'kernel': 'linear'}
0.614 (+/-0.240) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6661858974358974
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.639 (+/-0.396) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.326) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.664 (+/-0.374) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.326) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.668 (+/-0.378) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.656 (+/-0.384) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.326) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.668 (+/-0.378) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.656 (+/-0.384) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.664 (+/-0.388) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.689 (+/-0.436) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.689 (+/-0.436) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [1]
检查交叉验证集中测试集标签是否有预测不到的值: {-1}
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 1.00 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.589 (+/-0.232) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.639 (+/-0.236) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.616 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.639 (+/-0.236) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.240) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.616 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.639 (+/-0.236) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.614 (+/-0.241) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.616 (+/-0.238) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.14 0.17 0.15 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6389423076923078
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.664 (+/-0.388) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.383) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.639 (+/-0.396) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.326) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.698 (+/-0.383) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.655 (+/-0.378) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.326) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.698 (+/-0.383) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.662 (+/-0.383) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.656 (+/-0.384) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.326) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.698 (+/-0.383) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.379) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.656 (+/-0.384) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.747 (+/-0.449) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.723 (+/-0.417) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.689 (+/-0.374) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.698 (+/-0.383) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.681 (+/-0.372) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.659 (+/-0.375) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.653 (+/-0.379) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.315) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.588 (+/-0.232) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.666 (+/-0.315) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.637 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.315) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.637 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.240) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.666 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.662 (+/-0.314) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.614 (+/-0.241) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.641 (+/-0.236) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.666 (+/-0.314) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.666 (+/-0.315) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.665 (+/-0.314) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.663 (+/-0.313) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.661 (+/-0.314) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 0.9999999999999997, 'kernel': 'linear'}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6661858974358974
循环迭代之前,delta is: [3.33066907e-16]
SVC模型clf_op_iter的参数是: {'kernel': 'linear', 'decision_function_shape': 'ovr', 'class_weight': {-1: 30}, 'tol': 0.001, 'gamma': 'auto', 'random_state': 0, 'cache_size': 200, 'verbose': False, 'degree': 3, 'C': 0.9999999999999997, 'probability': False, 'shrinking': True, 'coef0': 0.0, 'max_iter': -1}
训练模型SVC对训练样本的预测准确率: 0.9016497461928934
测试集中,预测为舞弊样本的有: (array([ 370, 1248, 1251, 1252, 1255, 1256], dtype=int64),)
测试集中,实际为舞弊样本的有: (array([1246, 1247, 1248, 1249, 1250, 1251, 1252, 1253, 1254, 1255, 1256],
dtype=int64),)
预测的舞弊样本数目: 6
训练模型SVC对测试样本的预测准确率: 0.8851522842639594
以上是第14次特征筛选。
第14次特征筛选,AUC值是: 0.726871443163578
X_train_iter_svc.shape is: (1257, 38)
X_test_iter_svc.shape is: (1257, 38)
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.639 (+/-0.326) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.615 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6166666666666667
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.723 (+/-0.417) for {'C': 1.0, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.664 (+/-0.388) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.614 (+/-0.241) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6661858974358974
RBF的粗grid search最佳超参: {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0} RBF的粗grid search最高分值: 0.6166666666666667
linear的粗grid search最佳超参: {'C': 1.0, 'kernel': 'linear'} linear的粗grid search最高分值: 0.6661858974358974
粗grid search得到的parameter是:
[{'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['rbf'], 'gamma': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.639 (+/-0.396) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.326) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.664 (+/-0.374) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.326) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.668 (+/-0.378) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.656 (+/-0.384) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.326) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.668 (+/-0.378) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.656 (+/-0.384) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'linear'}
0.689 (+/-0.436) for {'C': 10.0, 'kernel': 'linear'}
0.656 (+/-0.384) for {'C': 100.0, 'kernel': 'linear'}
0.664 (+/-0.388) for {'C': 1000.0, 'kernel': 'linear'}
0.664 (+/-0.388) for {'C': 10000.0, 'kernel': 'linear'}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [1]
检查交叉验证集中测试集标签是否有预测不到的值: {-1}
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 1.00 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.589 (+/-0.232) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.639 (+/-0.236) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.616 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.639 (+/-0.236) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.240) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.616 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.639 (+/-0.236) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.614 (+/-0.241) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'linear'}
0.616 (+/-0.238) for {'C': 10.0, 'kernel': 'linear'}
0.613 (+/-0.241) for {'C': 100.0, 'kernel': 'linear'}
0.614 (+/-0.241) for {'C': 1000.0, 'kernel': 'linear'}
0.614 (+/-0.241) for {'C': 10000.0, 'kernel': 'linear'}
0.614 (+/-0.241) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.14 0.17 0.15 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6389423076923078
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.664 (+/-0.388) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.383) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.639 (+/-0.396) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.326) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.698 (+/-0.383) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.655 (+/-0.378) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.326) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.698 (+/-0.383) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.662 (+/-0.383) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.656 (+/-0.384) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.326) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.698 (+/-0.383) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.379) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.656 (+/-0.384) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.723 (+/-0.417) for {'C': 1.0, 'kernel': 'linear'}
0.653 (+/-0.379) for {'C': 10.0, 'kernel': 'linear'}
0.656 (+/-0.384) for {'C': 100.0, 'kernel': 'linear'}
0.664 (+/-0.388) for {'C': 1000.0, 'kernel': 'linear'}
0.664 (+/-0.388) for {'C': 10000.0, 'kernel': 'linear'}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [1]
检查交叉验证集中测试集标签是否有预测不到的值: {-1}
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 1.00 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.315) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.588 (+/-0.232) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.666 (+/-0.315) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.637 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.315) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.637 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.240) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.666 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.662 (+/-0.314) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.614 (+/-0.241) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.661 (+/-0.314) for {'C': 10.0, 'kernel': 'linear'}
0.613 (+/-0.241) for {'C': 100.0, 'kernel': 'linear'}
0.614 (+/-0.241) for {'C': 1000.0, 'kernel': 'linear'}
0.614 (+/-0.241) for {'C': 10000.0, 'kernel': 'linear'}
0.614 (+/-0.241) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6661858974358974
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.639 (+/-0.396) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.326) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.664 (+/-0.374) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.326) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.668 (+/-0.378) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.656 (+/-0.384) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.326) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.668 (+/-0.378) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.656 (+/-0.384) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.664 (+/-0.388) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.689 (+/-0.436) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.689 (+/-0.436) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [1]
检查交叉验证集中测试集标签是否有预测不到的值: {-1}
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 1.00 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.589 (+/-0.232) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.639 (+/-0.236) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.616 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.639 (+/-0.236) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.240) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.616 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.639 (+/-0.236) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.614 (+/-0.241) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.616 (+/-0.238) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.14 0.17 0.15 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6389423076923078
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.664 (+/-0.388) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.383) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.639 (+/-0.396) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.326) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.698 (+/-0.383) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.655 (+/-0.378) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.326) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.698 (+/-0.383) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.662 (+/-0.383) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.656 (+/-0.384) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.326) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.698 (+/-0.383) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.379) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.664 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.656 (+/-0.384) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.747 (+/-0.449) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.723 (+/-0.417) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.689 (+/-0.374) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.698 (+/-0.383) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.681 (+/-0.372) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.659 (+/-0.375) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.653 (+/-0.379) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.315) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.588 (+/-0.232) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.666 (+/-0.315) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.637 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.315) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.637 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.240) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.666 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.662 (+/-0.314) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.614 (+/-0.241) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.641 (+/-0.236) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.666 (+/-0.314) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.666 (+/-0.315) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.665 (+/-0.314) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.663 (+/-0.313) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.661 (+/-0.314) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 0.9999999999999997, 'kernel': 'linear'}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6661858974358974
循环迭代之前,delta is: [3.33066907e-16]
SVC模型clf_op_iter的参数是: {'kernel': 'linear', 'decision_function_shape': 'ovr', 'class_weight': {-1: 30}, 'tol': 0.001, 'gamma': 'auto', 'random_state': 0, 'cache_size': 200, 'verbose': False, 'degree': 3, 'C': 0.9999999999999997, 'probability': False, 'shrinking': True, 'coef0': 0.0, 'max_iter': -1}
训练模型SVC对训练样本的预测准确率: 0.9016497461928934
测试集中,预测为舞弊样本的有: (array([ 370, 1248, 1251, 1252, 1255, 1256], dtype=int64),)
测试集中,实际为舞弊样本的有: (array([1246, 1247, 1248, 1249, 1250, 1251, 1252, 1253, 1254, 1255, 1256],
dtype=int64),)
预测的舞弊样本数目: 6
训练模型SVC对测试样本的预测准确率: 0.8851522842639594
以上是第15次特征筛选。
第15次特征筛选,AUC值是: 0.726871443163578
X_train_iter_svc.shape is: (1257, 37)
X_test_iter_svc.shape is: (1257, 37)
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.639 (+/-0.326) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.615 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6166666666666667
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.722 (+/-0.417) for {'C': 1.0, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.651 (+/-0.385) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.641 (+/-0.235) for {'C': 1.0, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.613 (+/-0.241) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6411858974358975
RBF的粗grid search最佳超参: {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0} RBF的粗grid search最高分值: 0.6166666666666667
linear的粗grid search最佳超参: {'C': 1.0, 'kernel': 'linear'} linear的粗grid search最高分值: 0.6411858974358975
粗grid search得到的parameter是:
[{'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['rbf'], 'gamma': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.639 (+/-0.396) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.326) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.664 (+/-0.374) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.326) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.672 (+/-0.377) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.651 (+/-0.385) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.326) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.672 (+/-0.377) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.651 (+/-0.385) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.651 (+/-0.385) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'linear'}
0.689 (+/-0.436) for {'C': 10.0, 'kernel': 'linear'}
0.656 (+/-0.384) for {'C': 100.0, 'kernel': 'linear'}
0.651 (+/-0.385) for {'C': 1000.0, 'kernel': 'linear'}
0.651 (+/-0.385) for {'C': 10000.0, 'kernel': 'linear'}
0.651 (+/-0.385) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [1]
检查交叉验证集中测试集标签是否有预测不到的值: {-1}
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 1.00 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.589 (+/-0.232) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.639 (+/-0.236) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.616 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.639 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.613 (+/-0.240) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.616 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.639 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.613 (+/-0.241) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'linear'}
0.616 (+/-0.238) for {'C': 10.0, 'kernel': 'linear'}
0.613 (+/-0.242) for {'C': 100.0, 'kernel': 'linear'}
0.613 (+/-0.241) for {'C': 1000.0, 'kernel': 'linear'}
0.613 (+/-0.241) for {'C': 10000.0, 'kernel': 'linear'}
0.613 (+/-0.241) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.14 0.17 0.15 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6389423076923078
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.664 (+/-0.388) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.383) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.639 (+/-0.396) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.326) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.698 (+/-0.383) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.651 (+/-0.381) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.326) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.698 (+/-0.383) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.662 (+/-0.383) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.651 (+/-0.385) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.326) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.698 (+/-0.383) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.663 (+/-0.382) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.651 (+/-0.385) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.651 (+/-0.385) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.384) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.722 (+/-0.417) for {'C': 1.0, 'kernel': 'linear'}
0.654 (+/-0.379) for {'C': 10.0, 'kernel': 'linear'}
0.656 (+/-0.384) for {'C': 100.0, 'kernel': 'linear'}
0.651 (+/-0.385) for {'C': 1000.0, 'kernel': 'linear'}
0.651 (+/-0.385) for {'C': 10000.0, 'kernel': 'linear'}
0.651 (+/-0.385) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [1]
检查交叉验证集中测试集标签是否有预测不到的值: {-1}
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 1.00 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.315) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.588 (+/-0.233) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.666 (+/-0.315) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.637 (+/-0.236) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.315) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.662 (+/-0.312) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.613 (+/-0.240) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.666 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.662 (+/-0.313) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.613 (+/-0.241) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.641 (+/-0.235) for {'C': 1.0, 'kernel': 'linear'}
0.660 (+/-0.315) for {'C': 10.0, 'kernel': 'linear'}
0.613 (+/-0.242) for {'C': 100.0, 'kernel': 'linear'}
0.613 (+/-0.241) for {'C': 1000.0, 'kernel': 'linear'}
0.613 (+/-0.241) for {'C': 10000.0, 'kernel': 'linear'}
0.613 (+/-0.241) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6658653846153846
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.496 (+/-0.001) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.747 (+/-0.502) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.747 (+/-0.502) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.747 (+/-0.502) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.747 (+/-0.502) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.747 (+/-0.502) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.747 (+/-0.502) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.747 (+/-0.502) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.747 (+/-0.502) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.697 (+/-0.437) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.747 (+/-0.502) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.747 (+/-0.502) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.747 (+/-0.502) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.747 (+/-0.502) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.747 (+/-0.502) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.747 (+/-0.502) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.747 (+/-0.502) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.697 (+/-0.437) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.664 (+/-0.388) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.747 (+/-0.502) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.747 (+/-0.502) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.747 (+/-0.502) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.747 (+/-0.502) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.747 (+/-0.502) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.747 (+/-0.502) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.697 (+/-0.437) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.664 (+/-0.388) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.664 (+/-0.388) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.747 (+/-0.502) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.747 (+/-0.502) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.747 (+/-0.502) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.747 (+/-0.502) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.747 (+/-0.502) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.697 (+/-0.437) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.664 (+/-0.388) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.664 (+/-0.388) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.664 (+/-0.388) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.747 (+/-0.502) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.747 (+/-0.502) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.747 (+/-0.502) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.747 (+/-0.502) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.697 (+/-0.437) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.664 (+/-0.388) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.664 (+/-0.388) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.689 (+/-0.436) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.664 (+/-0.388) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.747 (+/-0.502) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.747 (+/-0.502) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.747 (+/-0.502) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.697 (+/-0.437) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.664 (+/-0.388) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.664 (+/-0.388) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.689 (+/-0.436) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.664 (+/-0.388) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.639 (+/-0.396) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.747 (+/-0.502) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.722 (+/-0.473) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.697 (+/-0.437) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.664 (+/-0.388) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.664 (+/-0.388) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.689 (+/-0.436) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.664 (+/-0.388) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.645 (+/-0.388) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.639 (+/-0.396) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.722 (+/-0.473) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.697 (+/-0.437) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.664 (+/-0.388) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.664 (+/-0.388) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.689 (+/-0.436) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.664 (+/-0.388) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.647 (+/-0.387) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.639 (+/-0.396) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.639 (+/-0.396) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.001}
0.722 (+/-0.473) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.697 (+/-0.437) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.664 (+/-0.388) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.689 (+/-0.436) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.689 (+/-0.436) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.689 (+/-0.436) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.647 (+/-0.387) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.639 (+/-0.396) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.639 (+/-0.396) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.639 (+/-0.396) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.437) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.664 (+/-0.388) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.689 (+/-0.436) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.689 (+/-0.436) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.689 (+/-0.436) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.647 (+/-0.387) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.646 (+/-0.388) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.639 (+/-0.396) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.639 (+/-0.396) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.656 (+/-0.384) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.664 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.689 (+/-0.436) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.689 (+/-0.436) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.689 (+/-0.436) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.664 (+/-0.374) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.646 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.645 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.639 (+/-0.396) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.639 (+/-0.396) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.656 (+/-0.384) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'linear'}
0.689 (+/-0.436) for {'C': 10.0, 'kernel': 'linear'}
0.656 (+/-0.384) for {'C': 100.0, 'kernel': 'linear'}
0.651 (+/-0.385) for {'C': 1000.0, 'kernel': 'linear'}
0.651 (+/-0.385) for {'C': 10000.0, 'kernel': 'linear'}
0.651 (+/-0.385) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [1]
检查交叉验证集中测试集标签是否有预测不到的值: {-1}
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 1.00 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.500 (+/-0.000) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.617 (+/-0.238) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.617 (+/-0.238) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.617 (+/-0.238) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.617 (+/-0.238) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.617 (+/-0.238) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.617 (+/-0.238) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.617 (+/-0.238) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.617 (+/-0.238) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.616 (+/-0.237) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.617 (+/-0.238) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.617 (+/-0.238) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.617 (+/-0.238) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.617 (+/-0.238) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.617 (+/-0.238) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.617 (+/-0.238) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.617 (+/-0.238) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.616 (+/-0.237) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.616 (+/-0.237) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.617 (+/-0.238) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.617 (+/-0.238) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.617 (+/-0.238) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.617 (+/-0.238) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.617 (+/-0.238) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.617 (+/-0.238) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.616 (+/-0.237) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.616 (+/-0.237) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.616 (+/-0.237) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.617 (+/-0.238) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.617 (+/-0.238) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.617 (+/-0.238) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.617 (+/-0.238) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.617 (+/-0.238) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.616 (+/-0.237) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.616 (+/-0.237) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.616 (+/-0.237) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.616 (+/-0.237) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.617 (+/-0.238) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.617 (+/-0.238) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.617 (+/-0.238) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.617 (+/-0.238) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.616 (+/-0.237) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.616 (+/-0.237) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.616 (+/-0.237) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.616 (+/-0.238) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.615 (+/-0.239) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.617 (+/-0.238) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.617 (+/-0.238) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.617 (+/-0.238) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.616 (+/-0.237) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.616 (+/-0.237) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.616 (+/-0.237) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.616 (+/-0.238) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.615 (+/-0.239) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.589 (+/-0.232) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.617 (+/-0.238) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.617 (+/-0.238) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.616 (+/-0.237) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.616 (+/-0.237) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.616 (+/-0.237) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.616 (+/-0.238) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.615 (+/-0.239) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.613 (+/-0.238) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.589 (+/-0.232) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.617 (+/-0.238) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.616 (+/-0.237) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.616 (+/-0.237) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.616 (+/-0.237) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.616 (+/-0.238) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.615 (+/-0.239) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.614 (+/-0.238) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.588 (+/-0.232) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.588 (+/-0.232) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.616 (+/-0.237) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.616 (+/-0.237) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.616 (+/-0.237) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.616 (+/-0.238) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.615 (+/-0.240) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.614 (+/-0.238) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.588 (+/-0.233) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.588 (+/-0.232) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.589 (+/-0.232) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.237) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.616 (+/-0.237) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.616 (+/-0.237) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.616 (+/-0.238) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.615 (+/-0.240) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.614 (+/-0.238) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.613 (+/-0.239) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.588 (+/-0.233) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.589 (+/-0.232) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.614 (+/-0.240) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.616 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.615 (+/-0.240) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.639 (+/-0.236) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.613 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.613 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.588 (+/-0.233) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.589 (+/-0.231) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.614 (+/-0.240) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'linear'}
0.616 (+/-0.238) for {'C': 10.0, 'kernel': 'linear'}
0.613 (+/-0.242) for {'C': 100.0, 'kernel': 'linear'}
0.613 (+/-0.241) for {'C': 1000.0, 'kernel': 'linear'}
0.613 (+/-0.241) for {'C': 10000.0, 'kernel': 'linear'}
0.613 (+/-0.241) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.14 0.17 0.15 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6387820512820513
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.747 (+/-0.502) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.747 (+/-0.502) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.747 (+/-0.502) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.747 (+/-0.502) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.747 (+/-0.502) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.747 (+/-0.502) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.747 (+/-0.502) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.697 (+/-0.437) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.664 (+/-0.388) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.664 (+/-0.388) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.747 (+/-0.502) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.747 (+/-0.502) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.747 (+/-0.502) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.747 (+/-0.502) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.747 (+/-0.502) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.697 (+/-0.437) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.664 (+/-0.388) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.664 (+/-0.388) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.674 (+/-0.375) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.747 (+/-0.502) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.747 (+/-0.502) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.747 (+/-0.502) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.747 (+/-0.502) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.697 (+/-0.437) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.681 (+/-0.372) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.689 (+/-0.374) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.681 (+/-0.372) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.641 (+/-0.307) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.747 (+/-0.502) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.747 (+/-0.502) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.747 (+/-0.502) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.747 (+/-0.449) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.681 (+/-0.372) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.698 (+/-0.383) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.681 (+/-0.372) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.635 (+/-0.308) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.619 (+/-0.321) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.747 (+/-0.502) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.747 (+/-0.502) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.747 (+/-0.449) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.681 (+/-0.372) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.698 (+/-0.383) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.681 (+/-0.372) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.641 (+/-0.306) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.644 (+/-0.390) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.647 (+/-0.387) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.747 (+/-0.502) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.747 (+/-0.449) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.681 (+/-0.372) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.698 (+/-0.383) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.681 (+/-0.372) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.645 (+/-0.306) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.648 (+/-0.386) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.646 (+/-0.388) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.639 (+/-0.396) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.747 (+/-0.449) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.681 (+/-0.372) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.698 (+/-0.383) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.681 (+/-0.372) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.645 (+/-0.306) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.654 (+/-0.379) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.652 (+/-0.385) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.645 (+/-0.388) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.639 (+/-0.396) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.449) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.681 (+/-0.372) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.698 (+/-0.383) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.681 (+/-0.372) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.645 (+/-0.306) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.653 (+/-0.379) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.649 (+/-0.386) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.644 (+/-0.389) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.639 (+/-0.396) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.639 (+/-0.396) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.449) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.001}
0.681 (+/-0.372) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.698 (+/-0.383) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.681 (+/-0.372) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.670 (+/-0.370) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.653 (+/-0.379) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.657 (+/-0.376) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.644 (+/-0.389) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.639 (+/-0.396) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.639 (+/-0.396) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.639 (+/-0.396) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.1}
0.681 (+/-0.372) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.383) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.681 (+/-0.372) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.666 (+/-0.371) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.653 (+/-0.379) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.657 (+/-0.376) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.653 (+/-0.379) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.646 (+/-0.388) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.639 (+/-0.396) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.639 (+/-0.396) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.656 (+/-0.384) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.1}
0.698 (+/-0.383) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.681 (+/-0.372) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.666 (+/-0.371) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.653 (+/-0.379) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.656 (+/-0.377) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.651 (+/-0.381) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.646 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.645 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.639 (+/-0.396) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.639 (+/-0.396) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.656 (+/-0.384) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.722 (+/-0.417) for {'C': 1.0, 'kernel': 'linear'}
0.654 (+/-0.379) for {'C': 10.0, 'kernel': 'linear'}
0.656 (+/-0.384) for {'C': 100.0, 'kernel': 'linear'}
0.651 (+/-0.385) for {'C': 1000.0, 'kernel': 'linear'}
0.651 (+/-0.385) for {'C': 10000.0, 'kernel': 'linear'}
0.651 (+/-0.385) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.617 (+/-0.238) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.617 (+/-0.238) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.617 (+/-0.238) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.617 (+/-0.238) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.617 (+/-0.238) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.617 (+/-0.238) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.617 (+/-0.238) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.616 (+/-0.237) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.616 (+/-0.237) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.616 (+/-0.237) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.617 (+/-0.238) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.617 (+/-0.238) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.617 (+/-0.238) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.617 (+/-0.238) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.617 (+/-0.238) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.616 (+/-0.237) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.616 (+/-0.237) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.616 (+/-0.237) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.640 (+/-0.234) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.617 (+/-0.238) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.617 (+/-0.238) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.617 (+/-0.238) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.617 (+/-0.238) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.616 (+/-0.237) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.641 (+/-0.235) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.641 (+/-0.235) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.665 (+/-0.313) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.639 (+/-0.235) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.617 (+/-0.238) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.617 (+/-0.238) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.617 (+/-0.238) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.641 (+/-0.236) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.666 (+/-0.314) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.666 (+/-0.315) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.665 (+/-0.313) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.639 (+/-0.235) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.612 (+/-0.240) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.617 (+/-0.238) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.617 (+/-0.238) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.641 (+/-0.236) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.666 (+/-0.314) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.666 (+/-0.315) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.665 (+/-0.313) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.664 (+/-0.313) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.612 (+/-0.240) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.613 (+/-0.240) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.617 (+/-0.238) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.641 (+/-0.236) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.666 (+/-0.314) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.666 (+/-0.315) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.665 (+/-0.313) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.664 (+/-0.313) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.636 (+/-0.325) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.613 (+/-0.240) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.588 (+/-0.233) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.641 (+/-0.236) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.666 (+/-0.314) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.666 (+/-0.315) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.665 (+/-0.313) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.664 (+/-0.313) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.661 (+/-0.314) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.637 (+/-0.325) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.613 (+/-0.239) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.589 (+/-0.232) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.001}
0.641 (+/-0.236) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.666 (+/-0.314) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.666 (+/-0.315) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.665 (+/-0.313) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.664 (+/-0.313) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.660 (+/-0.313) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.637 (+/-0.324) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.613 (+/-0.239) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.588 (+/-0.232) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.588 (+/-0.232) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.1}
0.641 (+/-0.236) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.314) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.666 (+/-0.315) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.665 (+/-0.313) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.664 (+/-0.313) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.660 (+/-0.314) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.662 (+/-0.314) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.612 (+/-0.239) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.588 (+/-0.233) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.588 (+/-0.232) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.589 (+/-0.232) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.1}
0.666 (+/-0.314) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.315) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.665 (+/-0.313) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.664 (+/-0.314) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.660 (+/-0.314) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.662 (+/-0.314) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.637 (+/-0.236) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.613 (+/-0.239) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.588 (+/-0.233) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.589 (+/-0.232) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.614 (+/-0.240) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.1}
0.666 (+/-0.315) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.665 (+/-0.313) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.664 (+/-0.314) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.660 (+/-0.314) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.661 (+/-0.313) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.637 (+/-0.236) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.613 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.613 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.588 (+/-0.233) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.589 (+/-0.231) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.614 (+/-0.240) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.641 (+/-0.235) for {'C': 1.0, 'kernel': 'linear'}
0.660 (+/-0.315) for {'C': 10.0, 'kernel': 'linear'}
0.613 (+/-0.242) for {'C': 100.0, 'kernel': 'linear'}
0.613 (+/-0.241) for {'C': 1000.0, 'kernel': 'linear'}
0.613 (+/-0.241) for {'C': 10000.0, 'kernel': 'linear'}
0.613 (+/-0.241) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6658653846153846
循环迭代之前,delta is: [6.01892829e+01 1.51188643e-02]
执行tunemodel()函数前,使用的grid search parameter是:
[{'C': array([25.11886432, 27.54228703, 30.1995172 , 33.11311215, 36.30780548,
39.81071706, 43.65158322, 47.86300923, 52.48074602, 57.54399373,
63.09573445]), 'kernel': ['rbf'], 'gamma': array([0.01584893, 0.01737801, 0.01905461, 0.02089296, 0.02290868,
0.02511886, 0.02754229, 0.03019952, 0.03311311, 0.03630781,
0.03981072])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.747 (+/-0.502) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.747 (+/-0.502) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.017378008287493755}
0.747 (+/-0.502) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.01905460717963247}
0.747 (+/-0.502) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.020892961308540393}
0.747 (+/-0.502) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.022908676527677727}
0.747 (+/-0.502) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.747 (+/-0.502) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.027542287033381657}
0.697 (+/-0.437) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.03019951720402017}
0.697 (+/-0.437) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.03311311214825912}
0.697 (+/-0.437) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.036307805477010145}
0.697 (+/-0.437) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.747 (+/-0.502) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.747 (+/-0.502) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 0.017378008287493755}
0.747 (+/-0.502) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 0.01905460717963247}
0.747 (+/-0.502) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 0.020892961308540393}
0.747 (+/-0.502) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 0.022908676527677727}
0.747 (+/-0.502) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.697 (+/-0.437) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 0.027542287033381657}
0.697 (+/-0.437) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 0.03019951720402017}
0.697 (+/-0.437) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 0.03311311214825912}
0.697 (+/-0.437) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 0.036307805477010145}
0.697 (+/-0.437) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.747 (+/-0.502) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.747 (+/-0.502) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 0.017378008287493755}
0.747 (+/-0.502) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 0.01905460717963247}
0.747 (+/-0.502) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 0.020892961308540393}
0.722 (+/-0.473) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 0.022908676527677727}
0.697 (+/-0.437) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.697 (+/-0.437) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 0.027542287033381657}
0.697 (+/-0.437) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 0.03019951720402017}
0.697 (+/-0.437) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 0.03311311214825912}
0.697 (+/-0.437) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 0.036307805477010145}
0.697 (+/-0.437) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.747 (+/-0.502) for {'C': 33.1131121482591, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.747 (+/-0.502) for {'C': 33.1131121482591, 'kernel': 'rbf', 'gamma': 0.017378008287493755}
0.747 (+/-0.502) for {'C': 33.1131121482591, 'kernel': 'rbf', 'gamma': 0.01905460717963247}
0.722 (+/-0.473) for {'C': 33.1131121482591, 'kernel': 'rbf', 'gamma': 0.020892961308540393}
0.697 (+/-0.437) for {'C': 33.1131121482591, 'kernel': 'rbf', 'gamma': 0.022908676527677727}
0.697 (+/-0.437) for {'C': 33.1131121482591, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.697 (+/-0.437) for {'C': 33.1131121482591, 'kernel': 'rbf', 'gamma': 0.027542287033381657}
0.697 (+/-0.437) for {'C': 33.1131121482591, 'kernel': 'rbf', 'gamma': 0.03019951720402017}
0.697 (+/-0.437) for {'C': 33.1131121482591, 'kernel': 'rbf', 'gamma': 0.03311311214825912}
0.697 (+/-0.437) for {'C': 33.1131121482591, 'kernel': 'rbf', 'gamma': 0.036307805477010145}
0.689 (+/-0.436) for {'C': 33.1131121482591, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.747 (+/-0.502) for {'C': 36.30780547701012, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.747 (+/-0.502) for {'C': 36.30780547701012, 'kernel': 'rbf', 'gamma': 0.017378008287493755}
0.697 (+/-0.437) for {'C': 36.30780547701012, 'kernel': 'rbf', 'gamma': 0.01905460717963247}
0.697 (+/-0.437) for {'C': 36.30780547701012, 'kernel': 'rbf', 'gamma': 0.020892961308540393}
0.697 (+/-0.437) for {'C': 36.30780547701012, 'kernel': 'rbf', 'gamma': 0.022908676527677727}
0.697 (+/-0.437) for {'C': 36.30780547701012, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.697 (+/-0.437) for {'C': 36.30780547701012, 'kernel': 'rbf', 'gamma': 0.027542287033381657}
0.697 (+/-0.437) for {'C': 36.30780547701012, 'kernel': 'rbf', 'gamma': 0.03019951720402017}
0.697 (+/-0.437) for {'C': 36.30780547701012, 'kernel': 'rbf', 'gamma': 0.03311311214825912}
0.689 (+/-0.436) for {'C': 36.30780547701012, 'kernel': 'rbf', 'gamma': 0.036307805477010145}
0.689 (+/-0.436) for {'C': 36.30780547701012, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.747 (+/-0.502) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.697 (+/-0.437) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.017378008287493755}
0.697 (+/-0.437) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.01905460717963247}
0.697 (+/-0.437) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.020892961308540393}
0.697 (+/-0.437) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.022908676527677727}
0.697 (+/-0.437) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.697 (+/-0.437) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.027542287033381657}
0.697 (+/-0.437) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.03019951720402017}
0.689 (+/-0.436) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.03311311214825912}
0.689 (+/-0.436) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.036307805477010145}
0.664 (+/-0.388) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.697 (+/-0.437) for {'C': 43.651583224016605, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.697 (+/-0.437) for {'C': 43.651583224016605, 'kernel': 'rbf', 'gamma': 0.017378008287493755}
0.697 (+/-0.437) for {'C': 43.651583224016605, 'kernel': 'rbf', 'gamma': 0.01905460717963247}
0.697 (+/-0.437) for {'C': 43.651583224016605, 'kernel': 'rbf', 'gamma': 0.020892961308540393}
0.697 (+/-0.437) for {'C': 43.651583224016605, 'kernel': 'rbf', 'gamma': 0.022908676527677727}
0.697 (+/-0.437) for {'C': 43.651583224016605, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.697 (+/-0.437) for {'C': 43.651583224016605, 'kernel': 'rbf', 'gamma': 0.027542287033381657}
0.689 (+/-0.436) for {'C': 43.651583224016605, 'kernel': 'rbf', 'gamma': 0.03019951720402017}
0.689 (+/-0.436) for {'C': 43.651583224016605, 'kernel': 'rbf', 'gamma': 0.03311311214825912}
0.664 (+/-0.388) for {'C': 43.651583224016605, 'kernel': 'rbf', 'gamma': 0.036307805477010145}
0.664 (+/-0.388) for {'C': 43.651583224016605, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.697 (+/-0.437) for {'C': 47.86300923226384, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.697 (+/-0.437) for {'C': 47.86300923226384, 'kernel': 'rbf', 'gamma': 0.017378008287493755}
0.697 (+/-0.437) for {'C': 47.86300923226384, 'kernel': 'rbf', 'gamma': 0.01905460717963247}
0.697 (+/-0.437) for {'C': 47.86300923226384, 'kernel': 'rbf', 'gamma': 0.020892961308540393}
0.697 (+/-0.437) for {'C': 47.86300923226384, 'kernel': 'rbf', 'gamma': 0.022908676527677727}
0.697 (+/-0.437) for {'C': 47.86300923226384, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.689 (+/-0.436) for {'C': 47.86300923226384, 'kernel': 'rbf', 'gamma': 0.027542287033381657}
0.689 (+/-0.436) for {'C': 47.86300923226384, 'kernel': 'rbf', 'gamma': 0.03019951720402017}
0.664 (+/-0.388) for {'C': 47.86300923226384, 'kernel': 'rbf', 'gamma': 0.03311311214825912}
0.664 (+/-0.388) for {'C': 47.86300923226384, 'kernel': 'rbf', 'gamma': 0.036307805477010145}
0.664 (+/-0.388) for {'C': 47.86300923226384, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.697 (+/-0.437) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.697 (+/-0.437) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.017378008287493755}
0.697 (+/-0.437) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.01905460717963247}
0.697 (+/-0.437) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.020892961308540393}
0.697 (+/-0.437) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.022908676527677727}
0.689 (+/-0.436) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.689 (+/-0.436) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.027542287033381657}
0.664 (+/-0.388) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.03019951720402017}
0.664 (+/-0.388) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.03311311214825912}
0.664 (+/-0.388) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.036307805477010145}
0.664 (+/-0.388) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.697 (+/-0.437) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.697 (+/-0.437) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.017378008287493755}
0.697 (+/-0.437) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.01905460717963247}
0.697 (+/-0.437) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.020892961308540393}
0.689 (+/-0.436) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.022908676527677727}
0.689 (+/-0.436) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.664 (+/-0.388) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.027542287033381657}
0.664 (+/-0.388) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.03019951720402017}
0.664 (+/-0.388) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.03311311214825912}
0.664 (+/-0.388) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.036307805477010145}
0.664 (+/-0.388) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.697 (+/-0.437) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.697 (+/-0.437) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.017378008287493755}
0.697 (+/-0.437) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.01905460717963247}
0.689 (+/-0.436) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.020892961308540393}
0.689 (+/-0.436) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.022908676527677727}
0.664 (+/-0.388) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.664 (+/-0.388) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.027542287033381657}
0.664 (+/-0.388) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.03019951720402017}
0.664 (+/-0.388) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.03311311214825912}
0.664 (+/-0.388) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.036307805477010145}
0.664 (+/-0.388) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'linear'}
0.689 (+/-0.436) for {'C': 10.0, 'kernel': 'linear'}
0.656 (+/-0.384) for {'C': 100.0, 'kernel': 'linear'}
0.651 (+/-0.385) for {'C': 1000.0, 'kernel': 'linear'}
0.651 (+/-0.385) for {'C': 10000.0, 'kernel': 'linear'}
0.651 (+/-0.385) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.617 (+/-0.238) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.617 (+/-0.238) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.017378008287493755}
0.617 (+/-0.238) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.01905460717963247}
0.617 (+/-0.238) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.020892961308540393}
0.617 (+/-0.238) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.022908676527677727}
0.617 (+/-0.238) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.617 (+/-0.238) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.027542287033381657}
0.616 (+/-0.237) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.03019951720402017}
0.616 (+/-0.237) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.03311311214825912}
0.616 (+/-0.237) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.036307805477010145}
0.616 (+/-0.237) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.617 (+/-0.238) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.617 (+/-0.238) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 0.017378008287493755}
0.617 (+/-0.238) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 0.01905460717963247}
0.617 (+/-0.238) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 0.020892961308540393}
0.617 (+/-0.238) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 0.022908676527677727}
0.617 (+/-0.238) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.616 (+/-0.237) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 0.027542287033381657}
0.616 (+/-0.237) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 0.03019951720402017}
0.616 (+/-0.237) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 0.03311311214825912}
0.616 (+/-0.237) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 0.036307805477010145}
0.616 (+/-0.237) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.617 (+/-0.238) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.617 (+/-0.238) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 0.017378008287493755}
0.617 (+/-0.238) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 0.01905460717963247}
0.617 (+/-0.238) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 0.020892961308540393}
0.617 (+/-0.238) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 0.022908676527677727}
0.616 (+/-0.237) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.616 (+/-0.237) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 0.027542287033381657}
0.616 (+/-0.237) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 0.03019951720402017}
0.616 (+/-0.237) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 0.03311311214825912}
0.616 (+/-0.237) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 0.036307805477010145}
0.616 (+/-0.237) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.617 (+/-0.238) for {'C': 33.1131121482591, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.617 (+/-0.238) for {'C': 33.1131121482591, 'kernel': 'rbf', 'gamma': 0.017378008287493755}
0.617 (+/-0.238) for {'C': 33.1131121482591, 'kernel': 'rbf', 'gamma': 0.01905460717963247}
0.617 (+/-0.238) for {'C': 33.1131121482591, 'kernel': 'rbf', 'gamma': 0.020892961308540393}
0.616 (+/-0.237) for {'C': 33.1131121482591, 'kernel': 'rbf', 'gamma': 0.022908676527677727}
0.616 (+/-0.237) for {'C': 33.1131121482591, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.616 (+/-0.237) for {'C': 33.1131121482591, 'kernel': 'rbf', 'gamma': 0.027542287033381657}
0.616 (+/-0.237) for {'C': 33.1131121482591, 'kernel': 'rbf', 'gamma': 0.03019951720402017}
0.616 (+/-0.237) for {'C': 33.1131121482591, 'kernel': 'rbf', 'gamma': 0.03311311214825912}
0.616 (+/-0.237) for {'C': 33.1131121482591, 'kernel': 'rbf', 'gamma': 0.036307805477010145}
0.616 (+/-0.237) for {'C': 33.1131121482591, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.617 (+/-0.238) for {'C': 36.30780547701012, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.617 (+/-0.238) for {'C': 36.30780547701012, 'kernel': 'rbf', 'gamma': 0.017378008287493755}
0.616 (+/-0.237) for {'C': 36.30780547701012, 'kernel': 'rbf', 'gamma': 0.01905460717963247}
0.616 (+/-0.237) for {'C': 36.30780547701012, 'kernel': 'rbf', 'gamma': 0.020892961308540393}
0.616 (+/-0.237) for {'C': 36.30780547701012, 'kernel': 'rbf', 'gamma': 0.022908676527677727}
0.616 (+/-0.237) for {'C': 36.30780547701012, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.616 (+/-0.237) for {'C': 36.30780547701012, 'kernel': 'rbf', 'gamma': 0.027542287033381657}
0.616 (+/-0.237) for {'C': 36.30780547701012, 'kernel': 'rbf', 'gamma': 0.03019951720402017}
0.616 (+/-0.237) for {'C': 36.30780547701012, 'kernel': 'rbf', 'gamma': 0.03311311214825912}
0.616 (+/-0.237) for {'C': 36.30780547701012, 'kernel': 'rbf', 'gamma': 0.036307805477010145}
0.616 (+/-0.237) for {'C': 36.30780547701012, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.617 (+/-0.238) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.616 (+/-0.237) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.017378008287493755}
0.616 (+/-0.237) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.01905460717963247}
0.616 (+/-0.237) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.020892961308540393}
0.616 (+/-0.237) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.022908676527677727}
0.616 (+/-0.237) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.616 (+/-0.237) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.027542287033381657}
0.616 (+/-0.237) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.03019951720402017}
0.616 (+/-0.237) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.03311311214825912}
0.616 (+/-0.237) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.036307805477010145}
0.616 (+/-0.237) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.616 (+/-0.237) for {'C': 43.651583224016605, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.616 (+/-0.237) for {'C': 43.651583224016605, 'kernel': 'rbf', 'gamma': 0.017378008287493755}
0.616 (+/-0.237) for {'C': 43.651583224016605, 'kernel': 'rbf', 'gamma': 0.01905460717963247}
0.616 (+/-0.237) for {'C': 43.651583224016605, 'kernel': 'rbf', 'gamma': 0.020892961308540393}
0.616 (+/-0.237) for {'C': 43.651583224016605, 'kernel': 'rbf', 'gamma': 0.022908676527677727}
0.616 (+/-0.237) for {'C': 43.651583224016605, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.616 (+/-0.237) for {'C': 43.651583224016605, 'kernel': 'rbf', 'gamma': 0.027542287033381657}
0.616 (+/-0.237) for {'C': 43.651583224016605, 'kernel': 'rbf', 'gamma': 0.03019951720402017}
0.616 (+/-0.237) for {'C': 43.651583224016605, 'kernel': 'rbf', 'gamma': 0.03311311214825912}
0.616 (+/-0.237) for {'C': 43.651583224016605, 'kernel': 'rbf', 'gamma': 0.036307805477010145}
0.616 (+/-0.237) for {'C': 43.651583224016605, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.616 (+/-0.237) for {'C': 47.86300923226384, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.616 (+/-0.237) for {'C': 47.86300923226384, 'kernel': 'rbf', 'gamma': 0.017378008287493755}
0.616 (+/-0.237) for {'C': 47.86300923226384, 'kernel': 'rbf', 'gamma': 0.01905460717963247}
0.616 (+/-0.237) for {'C': 47.86300923226384, 'kernel': 'rbf', 'gamma': 0.020892961308540393}
0.616 (+/-0.237) for {'C': 47.86300923226384, 'kernel': 'rbf', 'gamma': 0.022908676527677727}
0.616 (+/-0.237) for {'C': 47.86300923226384, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.616 (+/-0.237) for {'C': 47.86300923226384, 'kernel': 'rbf', 'gamma': 0.027542287033381657}
0.616 (+/-0.237) for {'C': 47.86300923226384, 'kernel': 'rbf', 'gamma': 0.03019951720402017}
0.616 (+/-0.237) for {'C': 47.86300923226384, 'kernel': 'rbf', 'gamma': 0.03311311214825912}
0.616 (+/-0.237) for {'C': 47.86300923226384, 'kernel': 'rbf', 'gamma': 0.036307805477010145}
0.616 (+/-0.237) for {'C': 47.86300923226384, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.616 (+/-0.237) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.616 (+/-0.237) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.017378008287493755}
0.616 (+/-0.237) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.01905460717963247}
0.616 (+/-0.237) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.020892961308540393}
0.616 (+/-0.237) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.022908676527677727}
0.616 (+/-0.237) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.616 (+/-0.237) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.027542287033381657}
0.616 (+/-0.237) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.03019951720402017}
0.616 (+/-0.237) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.03311311214825912}
0.616 (+/-0.237) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.036307805477010145}
0.616 (+/-0.237) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.616 (+/-0.237) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.616 (+/-0.237) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.017378008287493755}
0.616 (+/-0.237) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.01905460717963247}
0.616 (+/-0.237) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.020892961308540393}
0.616 (+/-0.237) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.022908676527677727}
0.616 (+/-0.237) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.616 (+/-0.237) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.027542287033381657}
0.616 (+/-0.237) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.03019951720402017}
0.616 (+/-0.237) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.03311311214825912}
0.616 (+/-0.237) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.036307805477010145}
0.616 (+/-0.237) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.616 (+/-0.237) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.616 (+/-0.237) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.017378008287493755}
0.616 (+/-0.237) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.01905460717963247}
0.616 (+/-0.237) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.020892961308540393}
0.616 (+/-0.237) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.022908676527677727}
0.616 (+/-0.237) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.616 (+/-0.237) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.027542287033381657}
0.616 (+/-0.237) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.03019951720402017}
0.616 (+/-0.237) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.03311311214825912}
0.616 (+/-0.237) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.036307805477010145}
0.616 (+/-0.237) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'linear'}
0.616 (+/-0.238) for {'C': 10.0, 'kernel': 'linear'}
0.613 (+/-0.242) for {'C': 100.0, 'kernel': 'linear'}
0.613 (+/-0.241) for {'C': 1000.0, 'kernel': 'linear'}
0.613 (+/-0.241) for {'C': 10000.0, 'kernel': 'linear'}
0.613 (+/-0.241) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6166666666666667
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.697 (+/-0.437) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.747 (+/-0.449) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.017378008287493755}
0.747 (+/-0.449) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.01905460717963247}
0.714 (+/-0.418) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.020892961308540393}
0.681 (+/-0.372) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.022908676527677727}
0.681 (+/-0.372) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.681 (+/-0.372) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.027542287033381657}
0.664 (+/-0.388) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.03019951720402017}
0.664 (+/-0.388) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.03311311214825912}
0.689 (+/-0.374) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.036307805477010145}
0.689 (+/-0.374) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.747 (+/-0.449) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.747 (+/-0.449) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 0.017378008287493755}
0.689 (+/-0.374) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 0.01905460717963247}
0.681 (+/-0.372) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 0.020892961308540393}
0.681 (+/-0.372) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 0.022908676527677727}
0.681 (+/-0.372) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.664 (+/-0.388) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 0.027542287033381657}
0.664 (+/-0.388) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 0.03019951720402017}
0.689 (+/-0.374) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 0.03311311214825912}
0.689 (+/-0.374) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 0.036307805477010145}
0.698 (+/-0.383) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.747 (+/-0.449) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.689 (+/-0.374) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 0.017378008287493755}
0.681 (+/-0.372) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 0.01905460717963247}
0.681 (+/-0.372) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 0.020892961308540393}
0.681 (+/-0.372) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 0.022908676527677727}
0.681 (+/-0.372) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.664 (+/-0.388) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 0.027542287033381657}
0.698 (+/-0.383) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 0.03019951720402017}
0.698 (+/-0.383) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 0.03311311214825912}
0.698 (+/-0.383) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 0.036307805477010145}
0.698 (+/-0.383) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.689 (+/-0.374) for {'C': 33.1131121482591, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.689 (+/-0.382) for {'C': 33.1131121482591, 'kernel': 'rbf', 'gamma': 0.017378008287493755}
0.681 (+/-0.372) for {'C': 33.1131121482591, 'kernel': 'rbf', 'gamma': 0.01905460717963247}
0.681 (+/-0.372) for {'C': 33.1131121482591, 'kernel': 'rbf', 'gamma': 0.020892961308540393}
0.681 (+/-0.372) for {'C': 33.1131121482591, 'kernel': 'rbf', 'gamma': 0.022908676527677727}
0.664 (+/-0.388) for {'C': 33.1131121482591, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.698 (+/-0.383) for {'C': 33.1131121482591, 'kernel': 'rbf', 'gamma': 0.027542287033381657}
0.698 (+/-0.383) for {'C': 33.1131121482591, 'kernel': 'rbf', 'gamma': 0.03019951720402017}
0.698 (+/-0.383) for {'C': 33.1131121482591, 'kernel': 'rbf', 'gamma': 0.03311311214825912}
0.698 (+/-0.383) for {'C': 33.1131121482591, 'kernel': 'rbf', 'gamma': 0.036307805477010145}
0.698 (+/-0.383) for {'C': 33.1131121482591, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.689 (+/-0.382) for {'C': 36.30780547701012, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.681 (+/-0.372) for {'C': 36.30780547701012, 'kernel': 'rbf', 'gamma': 0.017378008287493755}
0.681 (+/-0.372) for {'C': 36.30780547701012, 'kernel': 'rbf', 'gamma': 0.01905460717963247}
0.681 (+/-0.372) for {'C': 36.30780547701012, 'kernel': 'rbf', 'gamma': 0.020892961308540393}
0.681 (+/-0.372) for {'C': 36.30780547701012, 'kernel': 'rbf', 'gamma': 0.022908676527677727}
0.698 (+/-0.383) for {'C': 36.30780547701012, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.698 (+/-0.383) for {'C': 36.30780547701012, 'kernel': 'rbf', 'gamma': 0.027542287033381657}
0.698 (+/-0.383) for {'C': 36.30780547701012, 'kernel': 'rbf', 'gamma': 0.03019951720402017}
0.698 (+/-0.383) for {'C': 36.30780547701012, 'kernel': 'rbf', 'gamma': 0.03311311214825912}
0.698 (+/-0.383) for {'C': 36.30780547701012, 'kernel': 'rbf', 'gamma': 0.036307805477010145}
0.684 (+/-0.372) for {'C': 36.30780547701012, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.681 (+/-0.372) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.681 (+/-0.372) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.017378008287493755}
0.681 (+/-0.372) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.01905460717963247}
0.689 (+/-0.374) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.020892961308540393}
0.698 (+/-0.383) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.022908676527677727}
0.698 (+/-0.383) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.698 (+/-0.383) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.027542287033381657}
0.698 (+/-0.383) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.03019951720402017}
0.698 (+/-0.383) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.03311311214825912}
0.684 (+/-0.372) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.036307805477010145}
0.681 (+/-0.372) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.681 (+/-0.372) for {'C': 43.651583224016605, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.689 (+/-0.374) for {'C': 43.651583224016605, 'kernel': 'rbf', 'gamma': 0.017378008287493755}
0.689 (+/-0.374) for {'C': 43.651583224016605, 'kernel': 'rbf', 'gamma': 0.01905460717963247}
0.698 (+/-0.383) for {'C': 43.651583224016605, 'kernel': 'rbf', 'gamma': 0.020892961308540393}
0.698 (+/-0.383) for {'C': 43.651583224016605, 'kernel': 'rbf', 'gamma': 0.022908676527677727}
0.698 (+/-0.383) for {'C': 43.651583224016605, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.698 (+/-0.383) for {'C': 43.651583224016605, 'kernel': 'rbf', 'gamma': 0.027542287033381657}
0.698 (+/-0.383) for {'C': 43.651583224016605, 'kernel': 'rbf', 'gamma': 0.03019951720402017}
0.684 (+/-0.372) for {'C': 43.651583224016605, 'kernel': 'rbf', 'gamma': 0.03311311214825912}
0.681 (+/-0.372) for {'C': 43.651583224016605, 'kernel': 'rbf', 'gamma': 0.036307805477010145}
0.681 (+/-0.372) for {'C': 43.651583224016605, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.689 (+/-0.374) for {'C': 47.86300923226384, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.689 (+/-0.374) for {'C': 47.86300923226384, 'kernel': 'rbf', 'gamma': 0.017378008287493755}
0.698 (+/-0.383) for {'C': 47.86300923226384, 'kernel': 'rbf', 'gamma': 0.01905460717963247}
0.698 (+/-0.383) for {'C': 47.86300923226384, 'kernel': 'rbf', 'gamma': 0.020892961308540393}
0.698 (+/-0.383) for {'C': 47.86300923226384, 'kernel': 'rbf', 'gamma': 0.022908676527677727}
0.698 (+/-0.383) for {'C': 47.86300923226384, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.698 (+/-0.383) for {'C': 47.86300923226384, 'kernel': 'rbf', 'gamma': 0.027542287033381657}
0.684 (+/-0.372) for {'C': 47.86300923226384, 'kernel': 'rbf', 'gamma': 0.03019951720402017}
0.681 (+/-0.372) for {'C': 47.86300923226384, 'kernel': 'rbf', 'gamma': 0.03311311214825912}
0.681 (+/-0.372) for {'C': 47.86300923226384, 'kernel': 'rbf', 'gamma': 0.036307805477010145}
0.656 (+/-0.312) for {'C': 47.86300923226384, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.689 (+/-0.374) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.698 (+/-0.383) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.017378008287493755}
0.698 (+/-0.383) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.01905460717963247}
0.698 (+/-0.383) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.020892961308540393}
0.698 (+/-0.383) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.022908676527677727}
0.698 (+/-0.383) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.684 (+/-0.372) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.027542287033381657}
0.681 (+/-0.372) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.03019951720402017}
0.681 (+/-0.372) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.03311311214825912}
0.656 (+/-0.312) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.036307805477010145}
0.648 (+/-0.306) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.698 (+/-0.383) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.698 (+/-0.383) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.017378008287493755}
0.698 (+/-0.383) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.01905460717963247}
0.698 (+/-0.383) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.020892961308540393}
0.698 (+/-0.383) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.022908676527677727}
0.684 (+/-0.372) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.681 (+/-0.372) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.027542287033381657}
0.681 (+/-0.372) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.03019951720402017}
0.656 (+/-0.312) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.03311311214825912}
0.648 (+/-0.306) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.036307805477010145}
0.643 (+/-0.306) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.698 (+/-0.383) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.698 (+/-0.383) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.017378008287493755}
0.698 (+/-0.383) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.01905460717963247}
0.698 (+/-0.383) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.020892961308540393}
0.684 (+/-0.372) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.022908676527677727}
0.681 (+/-0.372) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.681 (+/-0.372) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.027542287033381657}
0.656 (+/-0.312) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.03019951720402017}
0.648 (+/-0.306) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.03311311214825912}
0.648 (+/-0.306) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.036307805477010145}
0.641 (+/-0.306) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.722 (+/-0.417) for {'C': 1.0, 'kernel': 'linear'}
0.654 (+/-0.379) for {'C': 10.0, 'kernel': 'linear'}
0.656 (+/-0.384) for {'C': 100.0, 'kernel': 'linear'}
0.651 (+/-0.385) for {'C': 1000.0, 'kernel': 'linear'}
0.651 (+/-0.385) for {'C': 10000.0, 'kernel': 'linear'}
0.651 (+/-0.385) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.616 (+/-0.237) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.641 (+/-0.236) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.017378008287493755}
0.641 (+/-0.236) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.01905460717963247}
0.641 (+/-0.235) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.020892961308540393}
0.641 (+/-0.235) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.022908676527677727}
0.641 (+/-0.235) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.641 (+/-0.235) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.027542287033381657}
0.616 (+/-0.237) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.03019951720402017}
0.616 (+/-0.237) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.03311311214825912}
0.641 (+/-0.235) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.036307805477010145}
0.641 (+/-0.235) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.641 (+/-0.236) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.641 (+/-0.236) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 0.017378008287493755}
0.641 (+/-0.235) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 0.01905460717963247}
0.641 (+/-0.235) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 0.020892961308540393}
0.641 (+/-0.235) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 0.022908676527677727}
0.641 (+/-0.235) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.616 (+/-0.237) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 0.027542287033381657}
0.616 (+/-0.237) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 0.03019951720402017}
0.641 (+/-0.235) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 0.03311311214825912}
0.641 (+/-0.235) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 0.036307805477010145}
0.666 (+/-0.315) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.641 (+/-0.236) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.641 (+/-0.235) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 0.017378008287493755}
0.641 (+/-0.235) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 0.01905460717963247}
0.641 (+/-0.235) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 0.020892961308540393}
0.641 (+/-0.235) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 0.022908676527677727}
0.641 (+/-0.235) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.616 (+/-0.237) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 0.027542287033381657}
0.666 (+/-0.315) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 0.03019951720402017}
0.666 (+/-0.315) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 0.03311311214825912}
0.666 (+/-0.315) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 0.036307805477010145}
0.666 (+/-0.315) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.641 (+/-0.235) for {'C': 33.1131121482591, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.666 (+/-0.315) for {'C': 33.1131121482591, 'kernel': 'rbf', 'gamma': 0.017378008287493755}
0.641 (+/-0.235) for {'C': 33.1131121482591, 'kernel': 'rbf', 'gamma': 0.01905460717963247}
0.641 (+/-0.235) for {'C': 33.1131121482591, 'kernel': 'rbf', 'gamma': 0.020892961308540393}
0.641 (+/-0.235) for {'C': 33.1131121482591, 'kernel': 'rbf', 'gamma': 0.022908676527677727}
0.616 (+/-0.237) for {'C': 33.1131121482591, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.666 (+/-0.315) for {'C': 33.1131121482591, 'kernel': 'rbf', 'gamma': 0.027542287033381657}
0.666 (+/-0.315) for {'C': 33.1131121482591, 'kernel': 'rbf', 'gamma': 0.03019951720402017}
0.666 (+/-0.315) for {'C': 33.1131121482591, 'kernel': 'rbf', 'gamma': 0.03311311214825912}
0.666 (+/-0.315) for {'C': 33.1131121482591, 'kernel': 'rbf', 'gamma': 0.036307805477010145}
0.666 (+/-0.315) for {'C': 33.1131121482591, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.666 (+/-0.315) for {'C': 36.30780547701012, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.666 (+/-0.314) for {'C': 36.30780547701012, 'kernel': 'rbf', 'gamma': 0.017378008287493755}
0.641 (+/-0.235) for {'C': 36.30780547701012, 'kernel': 'rbf', 'gamma': 0.01905460717963247}
0.641 (+/-0.235) for {'C': 36.30780547701012, 'kernel': 'rbf', 'gamma': 0.020892961308540393}
0.641 (+/-0.235) for {'C': 36.30780547701012, 'kernel': 'rbf', 'gamma': 0.022908676527677727}
0.666 (+/-0.315) for {'C': 36.30780547701012, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.666 (+/-0.315) for {'C': 36.30780547701012, 'kernel': 'rbf', 'gamma': 0.027542287033381657}
0.666 (+/-0.315) for {'C': 36.30780547701012, 'kernel': 'rbf', 'gamma': 0.03019951720402017}
0.666 (+/-0.315) for {'C': 36.30780547701012, 'kernel': 'rbf', 'gamma': 0.03311311214825912}
0.666 (+/-0.315) for {'C': 36.30780547701012, 'kernel': 'rbf', 'gamma': 0.036307805477010145}
0.665 (+/-0.314) for {'C': 36.30780547701012, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.666 (+/-0.314) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.641 (+/-0.235) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.017378008287493755}
0.641 (+/-0.235) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.01905460717963247}
0.666 (+/-0.314) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.020892961308540393}
0.666 (+/-0.315) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.022908676527677727}
0.666 (+/-0.315) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.666 (+/-0.315) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.027542287033381657}
0.666 (+/-0.315) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.03019951720402017}
0.666 (+/-0.315) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.03311311214825912}
0.665 (+/-0.314) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.036307805477010145}
0.665 (+/-0.313) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.641 (+/-0.235) for {'C': 43.651583224016605, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.666 (+/-0.314) for {'C': 43.651583224016605, 'kernel': 'rbf', 'gamma': 0.017378008287493755}
0.666 (+/-0.314) for {'C': 43.651583224016605, 'kernel': 'rbf', 'gamma': 0.01905460717963247}
0.666 (+/-0.315) for {'C': 43.651583224016605, 'kernel': 'rbf', 'gamma': 0.020892961308540393}
0.666 (+/-0.315) for {'C': 43.651583224016605, 'kernel': 'rbf', 'gamma': 0.022908676527677727}
0.666 (+/-0.315) for {'C': 43.651583224016605, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.666 (+/-0.315) for {'C': 43.651583224016605, 'kernel': 'rbf', 'gamma': 0.027542287033381657}
0.666 (+/-0.315) for {'C': 43.651583224016605, 'kernel': 'rbf', 'gamma': 0.03019951720402017}
0.665 (+/-0.314) for {'C': 43.651583224016605, 'kernel': 'rbf', 'gamma': 0.03311311214825912}
0.665 (+/-0.313) for {'C': 43.651583224016605, 'kernel': 'rbf', 'gamma': 0.036307805477010145}
0.665 (+/-0.313) for {'C': 43.651583224016605, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.666 (+/-0.314) for {'C': 47.86300923226384, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.666 (+/-0.314) for {'C': 47.86300923226384, 'kernel': 'rbf', 'gamma': 0.017378008287493755}
0.666 (+/-0.315) for {'C': 47.86300923226384, 'kernel': 'rbf', 'gamma': 0.01905460717963247}
0.666 (+/-0.315) for {'C': 47.86300923226384, 'kernel': 'rbf', 'gamma': 0.020892961308540393}
0.666 (+/-0.315) for {'C': 47.86300923226384, 'kernel': 'rbf', 'gamma': 0.022908676527677727}
0.666 (+/-0.315) for {'C': 47.86300923226384, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.666 (+/-0.315) for {'C': 47.86300923226384, 'kernel': 'rbf', 'gamma': 0.027542287033381657}
0.665 (+/-0.314) for {'C': 47.86300923226384, 'kernel': 'rbf', 'gamma': 0.03019951720402017}
0.665 (+/-0.313) for {'C': 47.86300923226384, 'kernel': 'rbf', 'gamma': 0.03311311214825912}
0.665 (+/-0.313) for {'C': 47.86300923226384, 'kernel': 'rbf', 'gamma': 0.036307805477010145}
0.665 (+/-0.313) for {'C': 47.86300923226384, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.666 (+/-0.314) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.666 (+/-0.315) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.017378008287493755}
0.666 (+/-0.315) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.01905460717963247}
0.666 (+/-0.315) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.020892961308540393}
0.666 (+/-0.315) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.022908676527677727}
0.666 (+/-0.315) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.665 (+/-0.314) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.027542287033381657}
0.665 (+/-0.313) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.03019951720402017}
0.665 (+/-0.313) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.03311311214825912}
0.665 (+/-0.313) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.036307805477010145}
0.665 (+/-0.314) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.666 (+/-0.315) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.666 (+/-0.315) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.017378008287493755}
0.666 (+/-0.315) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.01905460717963247}
0.666 (+/-0.315) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.020892961308540393}
0.666 (+/-0.315) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.022908676527677727}
0.665 (+/-0.314) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.665 (+/-0.313) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.027542287033381657}
0.665 (+/-0.313) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.03019951720402017}
0.665 (+/-0.313) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.03311311214825912}
0.665 (+/-0.314) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.036307805477010145}
0.664 (+/-0.314) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.666 (+/-0.315) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.666 (+/-0.315) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.017378008287493755}
0.666 (+/-0.315) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.01905460717963247}
0.666 (+/-0.315) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.020892961308540393}
0.665 (+/-0.314) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.022908676527677727}
0.665 (+/-0.313) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.665 (+/-0.313) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.027542287033381657}
0.665 (+/-0.313) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.03019951720402017}
0.665 (+/-0.314) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.03311311214825912}
0.664 (+/-0.314) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.036307805477010145}
0.664 (+/-0.313) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.641 (+/-0.235) for {'C': 1.0, 'kernel': 'linear'}
0.660 (+/-0.315) for {'C': 10.0, 'kernel': 'linear'}
0.613 (+/-0.242) for {'C': 100.0, 'kernel': 'linear'}
0.613 (+/-0.241) for {'C': 1000.0, 'kernel': 'linear'}
0.613 (+/-0.241) for {'C': 10000.0, 'kernel': 'linear'}
0.613 (+/-0.241) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6658653846153846
发现最优参数gamma为原先的最大/最小值,直接重新设置超参。
第0轮loop中,tunemodel函数得到的最优参数是: ([{'C': array([25.11886432, 25.58585887, 26.0615355 , 26.54605562, 27.03958364,
27.54228703, 28.05433638, 28.57590543, 29.10717118, 29.6483139 ,
30.1995172 ]), 'kernel': ['rbf'], 'gamma': array([3.98107171e-07, 3.98107171e-06, 3.98107171e-05, 3.98107171e-04,
3.98107171e-03, 3.98107171e-02, 3.98107171e-01, 3.98107171e+00,
3.98107171e+01, 3.98107171e+02, 3.98107171e+03])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}], {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 0.039810717055349734}, 0.6658653846153846)
这是第0次迭代微调C和gamma。
第0次迭代,得到delta: [12.26843002 0.01469185]
执行tunemodel()函数前,使用的grid search parameter是:
[{'C': array([25.11886432, 25.58585887, 26.0615355 , 26.54605562, 27.03958364,
27.54228703, 28.05433638, 28.57590543, 29.10717118, 29.6483139 ,
30.1995172 ]), 'kernel': ['rbf'], 'gamma': array([3.98107171e-07, 3.98107171e-06, 3.98107171e-05, 3.98107171e-04,
3.98107171e-03, 3.98107171e-02, 3.98107171e-01, 3.98107171e+00,
3.98107171e+01, 3.98107171e+02, 3.98107171e+03])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.496 (+/-0.001) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 3.981071705534969e-07}
0.496 (+/-0.001) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 3.981071705534969e-06}
0.496 (+/-0.001) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 3.9810717055349695e-05}
0.496 (+/-0.001) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.0003981071705534969}
0.747 (+/-0.502) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.003981071705534969}
0.697 (+/-0.437) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.03981071705534969}
0.626 (+/-0.318) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.3981071705534969}
0.496 (+/-0.001) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 3.981071705534969}
0.496 (+/-0.001) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 39.81071705534969}
0.496 (+/-0.001) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 398.1071705534969}
0.496 (+/-0.001) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 3981.0717055349733}
0.496 (+/-0.001) for {'C': 25.58585886905646, 'kernel': 'rbf', 'gamma': 3.981071705534969e-07}
0.496 (+/-0.001) for {'C': 25.58585886905646, 'kernel': 'rbf', 'gamma': 3.981071705534969e-06}
0.496 (+/-0.001) for {'C': 25.58585886905646, 'kernel': 'rbf', 'gamma': 3.9810717055349695e-05}
0.496 (+/-0.001) for {'C': 25.58585886905646, 'kernel': 'rbf', 'gamma': 0.0003981071705534969}
0.747 (+/-0.502) for {'C': 25.58585886905646, 'kernel': 'rbf', 'gamma': 0.003981071705534969}
0.697 (+/-0.437) for {'C': 25.58585886905646, 'kernel': 'rbf', 'gamma': 0.03981071705534969}
0.626 (+/-0.318) for {'C': 25.58585886905646, 'kernel': 'rbf', 'gamma': 0.3981071705534969}
0.496 (+/-0.001) for {'C': 25.58585886905646, 'kernel': 'rbf', 'gamma': 3.981071705534969}
0.496 (+/-0.001) for {'C': 25.58585886905646, 'kernel': 'rbf', 'gamma': 39.81071705534969}
0.496 (+/-0.001) for {'C': 25.58585886905646, 'kernel': 'rbf', 'gamma': 398.1071705534969}
0.496 (+/-0.001) for {'C': 25.58585886905646, 'kernel': 'rbf', 'gamma': 3981.0717055349733}
0.496 (+/-0.001) for {'C': 26.061535499988945, 'kernel': 'rbf', 'gamma': 3.981071705534969e-07}
0.496 (+/-0.001) for {'C': 26.061535499988945, 'kernel': 'rbf', 'gamma': 3.981071705534969e-06}
0.496 (+/-0.001) for {'C': 26.061535499988945, 'kernel': 'rbf', 'gamma': 3.9810717055349695e-05}
0.496 (+/-0.001) for {'C': 26.061535499988945, 'kernel': 'rbf', 'gamma': 0.0003981071705534969}
0.747 (+/-0.502) for {'C': 26.061535499988945, 'kernel': 'rbf', 'gamma': 0.003981071705534969}
0.697 (+/-0.437) for {'C': 26.061535499988945, 'kernel': 'rbf', 'gamma': 0.03981071705534969}
0.626 (+/-0.318) for {'C': 26.061535499988945, 'kernel': 'rbf', 'gamma': 0.3981071705534969}
0.496 (+/-0.001) for {'C': 26.061535499988945, 'kernel': 'rbf', 'gamma': 3.981071705534969}
0.496 (+/-0.001) for {'C': 26.061535499988945, 'kernel': 'rbf', 'gamma': 39.81071705534969}
0.496 (+/-0.001) for {'C': 26.061535499988945, 'kernel': 'rbf', 'gamma': 398.1071705534969}
0.496 (+/-0.001) for {'C': 26.061535499988945, 'kernel': 'rbf', 'gamma': 3981.0717055349733}
0.496 (+/-0.001) for {'C': 26.54605561975539, 'kernel': 'rbf', 'gamma': 3.981071705534969e-07}
0.496 (+/-0.001) for {'C': 26.54605561975539, 'kernel': 'rbf', 'gamma': 3.981071705534969e-06}
0.496 (+/-0.001) for {'C': 26.54605561975539, 'kernel': 'rbf', 'gamma': 3.9810717055349695e-05}
0.496 (+/-0.001) for {'C': 26.54605561975539, 'kernel': 'rbf', 'gamma': 0.0003981071705534969}
0.747 (+/-0.502) for {'C': 26.54605561975539, 'kernel': 'rbf', 'gamma': 0.003981071705534969}
0.697 (+/-0.437) for {'C': 26.54605561975539, 'kernel': 'rbf', 'gamma': 0.03981071705534969}
0.631 (+/-0.319) for {'C': 26.54605561975539, 'kernel': 'rbf', 'gamma': 0.3981071705534969}
0.496 (+/-0.001) for {'C': 26.54605561975539, 'kernel': 'rbf', 'gamma': 3.981071705534969}
0.496 (+/-0.001) for {'C': 26.54605561975539, 'kernel': 'rbf', 'gamma': 39.81071705534969}
0.496 (+/-0.001) for {'C': 26.54605561975539, 'kernel': 'rbf', 'gamma': 398.1071705534969}
0.496 (+/-0.001) for {'C': 26.54605561975539, 'kernel': 'rbf', 'gamma': 3981.0717055349733}
0.496 (+/-0.001) for {'C': 27.039583641088424, 'kernel': 'rbf', 'gamma': 3.981071705534969e-07}
0.496 (+/-0.001) for {'C': 27.039583641088424, 'kernel': 'rbf', 'gamma': 3.981071705534969e-06}
0.496 (+/-0.001) for {'C': 27.039583641088424, 'kernel': 'rbf', 'gamma': 3.9810717055349695e-05}
0.496 (+/-0.001) for {'C': 27.039583641088424, 'kernel': 'rbf', 'gamma': 0.0003981071705534969}
0.747 (+/-0.502) for {'C': 27.039583641088424, 'kernel': 'rbf', 'gamma': 0.003981071705534969}
0.697 (+/-0.437) for {'C': 27.039583641088424, 'kernel': 'rbf', 'gamma': 0.03981071705534969}
0.631 (+/-0.319) for {'C': 27.039583641088424, 'kernel': 'rbf', 'gamma': 0.3981071705534969}
0.496 (+/-0.001) for {'C': 27.039583641088424, 'kernel': 'rbf', 'gamma': 3.981071705534969}
0.496 (+/-0.001) for {'C': 27.039583641088424, 'kernel': 'rbf', 'gamma': 39.81071705534969}
0.496 (+/-0.001) for {'C': 27.039583641088424, 'kernel': 'rbf', 'gamma': 398.1071705534969}
0.496 (+/-0.001) for {'C': 27.039583641088424, 'kernel': 'rbf', 'gamma': 3981.0717055349733}
0.496 (+/-0.001) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 3.981071705534969e-07}
0.496 (+/-0.001) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 3.981071705534969e-06}
0.496 (+/-0.001) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 3.9810717055349695e-05}
0.496 (+/-0.001) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 0.0003981071705534969}
0.747 (+/-0.502) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 0.003981071705534969}
0.697 (+/-0.437) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 0.03981071705534969}
0.631 (+/-0.319) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 0.3981071705534969}
0.496 (+/-0.001) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 3.981071705534969}
0.496 (+/-0.001) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 39.81071705534969}
0.496 (+/-0.001) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 398.1071705534969}
0.496 (+/-0.001) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 3981.0717055349733}
0.496 (+/-0.001) for {'C': 28.054336379517135, 'kernel': 'rbf', 'gamma': 3.981071705534969e-07}
0.496 (+/-0.001) for {'C': 28.054336379517135, 'kernel': 'rbf', 'gamma': 3.981071705534969e-06}
0.496 (+/-0.001) for {'C': 28.054336379517135, 'kernel': 'rbf', 'gamma': 3.9810717055349695e-05}
0.496 (+/-0.001) for {'C': 28.054336379517135, 'kernel': 'rbf', 'gamma': 0.0003981071705534969}
0.747 (+/-0.502) for {'C': 28.054336379517135, 'kernel': 'rbf', 'gamma': 0.003981071705534969}
0.697 (+/-0.437) for {'C': 28.054336379517135, 'kernel': 'rbf', 'gamma': 0.03981071705534969}
0.631 (+/-0.319) for {'C': 28.054336379517135, 'kernel': 'rbf', 'gamma': 0.3981071705534969}
0.496 (+/-0.001) for {'C': 28.054336379517135, 'kernel': 'rbf', 'gamma': 3.981071705534969}
0.496 (+/-0.001) for {'C': 28.054336379517135, 'kernel': 'rbf', 'gamma': 39.81071705534969}
0.496 (+/-0.001) for {'C': 28.054336379517135, 'kernel': 'rbf', 'gamma': 398.1071705534969}
0.496 (+/-0.001) for {'C': 28.054336379517135, 'kernel': 'rbf', 'gamma': 3981.0717055349733}
0.496 (+/-0.001) for {'C': 28.57590543374946, 'kernel': 'rbf', 'gamma': 3.981071705534969e-07}
0.496 (+/-0.001) for {'C': 28.57590543374946, 'kernel': 'rbf', 'gamma': 3.981071705534969e-06}
0.496 (+/-0.001) for {'C': 28.57590543374946, 'kernel': 'rbf', 'gamma': 3.9810717055349695e-05}
0.496 (+/-0.001) for {'C': 28.57590543374946, 'kernel': 'rbf', 'gamma': 0.0003981071705534969}
0.747 (+/-0.502) for {'C': 28.57590543374946, 'kernel': 'rbf', 'gamma': 0.003981071705534969}
0.697 (+/-0.437) for {'C': 28.57590543374946, 'kernel': 'rbf', 'gamma': 0.03981071705534969}
0.631 (+/-0.319) for {'C': 28.57590543374946, 'kernel': 'rbf', 'gamma': 0.3981071705534969}
0.496 (+/-0.001) for {'C': 28.57590543374946, 'kernel': 'rbf', 'gamma': 3.981071705534969}
0.496 (+/-0.001) for {'C': 28.57590543374946, 'kernel': 'rbf', 'gamma': 39.81071705534969}
0.496 (+/-0.001) for {'C': 28.57590543374946, 'kernel': 'rbf', 'gamma': 398.1071705534969}
0.496 (+/-0.001) for {'C': 28.57590543374946, 'kernel': 'rbf', 'gamma': 3981.0717055349733}
0.496 (+/-0.001) for {'C': 29.10717118066605, 'kernel': 'rbf', 'gamma': 3.981071705534969e-07}
0.496 (+/-0.001) for {'C': 29.10717118066605, 'kernel': 'rbf', 'gamma': 3.981071705534969e-06}
0.496 (+/-0.001) for {'C': 29.10717118066605, 'kernel': 'rbf', 'gamma': 3.9810717055349695e-05}
0.496 (+/-0.001) for {'C': 29.10717118066605, 'kernel': 'rbf', 'gamma': 0.0003981071705534969}
0.747 (+/-0.502) for {'C': 29.10717118066605, 'kernel': 'rbf', 'gamma': 0.003981071705534969}
0.697 (+/-0.437) for {'C': 29.10717118066605, 'kernel': 'rbf', 'gamma': 0.03981071705534969}
0.631 (+/-0.319) for {'C': 29.10717118066605, 'kernel': 'rbf', 'gamma': 0.3981071705534969}
0.496 (+/-0.001) for {'C': 29.10717118066605, 'kernel': 'rbf', 'gamma': 3.981071705534969}
0.496 (+/-0.001) for {'C': 29.10717118066605, 'kernel': 'rbf', 'gamma': 39.81071705534969}
0.496 (+/-0.001) for {'C': 29.10717118066605, 'kernel': 'rbf', 'gamma': 398.1071705534969}
0.496 (+/-0.001) for {'C': 29.10717118066605, 'kernel': 'rbf', 'gamma': 3981.0717055349733}
0.496 (+/-0.001) for {'C': 29.648313895243408, 'kernel': 'rbf', 'gamma': 3.981071705534969e-07}
0.496 (+/-0.001) for {'C': 29.648313895243408, 'kernel': 'rbf', 'gamma': 3.981071705534969e-06}
0.496 (+/-0.001) for {'C': 29.648313895243408, 'kernel': 'rbf', 'gamma': 3.9810717055349695e-05}
0.496 (+/-0.001) for {'C': 29.648313895243408, 'kernel': 'rbf', 'gamma': 0.0003981071705534969}
0.747 (+/-0.502) for {'C': 29.648313895243408, 'kernel': 'rbf', 'gamma': 0.003981071705534969}
0.697 (+/-0.437) for {'C': 29.648313895243408, 'kernel': 'rbf', 'gamma': 0.03981071705534969}
0.631 (+/-0.319) for {'C': 29.648313895243408, 'kernel': 'rbf', 'gamma': 0.3981071705534969}
0.496 (+/-0.001) for {'C': 29.648313895243408, 'kernel': 'rbf', 'gamma': 3.981071705534969}
0.496 (+/-0.001) for {'C': 29.648313895243408, 'kernel': 'rbf', 'gamma': 39.81071705534969}
0.496 (+/-0.001) for {'C': 29.648313895243408, 'kernel': 'rbf', 'gamma': 398.1071705534969}
0.496 (+/-0.001) for {'C': 29.648313895243408, 'kernel': 'rbf', 'gamma': 3981.0717055349733}
0.496 (+/-0.001) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 3.981071705534969e-07}
0.496 (+/-0.001) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 3.981071705534969e-06}
0.496 (+/-0.001) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 3.9810717055349695e-05}
0.496 (+/-0.001) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 0.0003981071705534969}
0.747 (+/-0.502) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 0.003981071705534969}
0.697 (+/-0.437) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 0.03981071705534969}
0.631 (+/-0.319) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 0.3981071705534969}
0.496 (+/-0.001) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 3.981071705534969}
0.496 (+/-0.001) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 39.81071705534969}
0.496 (+/-0.001) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 398.1071705534969}
0.496 (+/-0.001) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 3981.0717055349733}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'linear'}
0.689 (+/-0.436) for {'C': 10.0, 'kernel': 'linear'}
0.656 (+/-0.384) for {'C': 100.0, 'kernel': 'linear'}
0.651 (+/-0.385) for {'C': 1000.0, 'kernel': 'linear'}
0.651 (+/-0.385) for {'C': 10000.0, 'kernel': 'linear'}
0.651 (+/-0.385) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.500 (+/-0.000) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 3.981071705534969e-07}
0.500 (+/-0.000) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 3.981071705534969e-06}
0.500 (+/-0.000) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 3.9810717055349695e-05}
0.500 (+/-0.000) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.0003981071705534969}
0.617 (+/-0.238) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.003981071705534969}
0.616 (+/-0.237) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.03981071705534969}
0.614 (+/-0.239) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.3981071705534969}
0.499 (+/-0.002) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 3.981071705534969}
0.500 (+/-0.000) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 39.81071705534969}
0.500 (+/-0.000) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 398.1071705534969}
0.500 (+/-0.000) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 3981.0717055349733}
0.500 (+/-0.000) for {'C': 25.58585886905646, 'kernel': 'rbf', 'gamma': 3.981071705534969e-07}
0.500 (+/-0.000) for {'C': 25.58585886905646, 'kernel': 'rbf', 'gamma': 3.981071705534969e-06}
0.500 (+/-0.000) for {'C': 25.58585886905646, 'kernel': 'rbf', 'gamma': 3.9810717055349695e-05}
0.500 (+/-0.000) for {'C': 25.58585886905646, 'kernel': 'rbf', 'gamma': 0.0003981071705534969}
0.617 (+/-0.238) for {'C': 25.58585886905646, 'kernel': 'rbf', 'gamma': 0.003981071705534969}
0.616 (+/-0.237) for {'C': 25.58585886905646, 'kernel': 'rbf', 'gamma': 0.03981071705534969}
0.613 (+/-0.240) for {'C': 25.58585886905646, 'kernel': 'rbf', 'gamma': 0.3981071705534969}
0.499 (+/-0.002) for {'C': 25.58585886905646, 'kernel': 'rbf', 'gamma': 3.981071705534969}
0.500 (+/-0.000) for {'C': 25.58585886905646, 'kernel': 'rbf', 'gamma': 39.81071705534969}
0.500 (+/-0.000) for {'C': 25.58585886905646, 'kernel': 'rbf', 'gamma': 398.1071705534969}
0.500 (+/-0.000) for {'C': 25.58585886905646, 'kernel': 'rbf', 'gamma': 3981.0717055349733}
0.500 (+/-0.000) for {'C': 26.061535499988945, 'kernel': 'rbf', 'gamma': 3.981071705534969e-07}
0.500 (+/-0.000) for {'C': 26.061535499988945, 'kernel': 'rbf', 'gamma': 3.981071705534969e-06}
0.500 (+/-0.000) for {'C': 26.061535499988945, 'kernel': 'rbf', 'gamma': 3.9810717055349695e-05}
0.500 (+/-0.000) for {'C': 26.061535499988945, 'kernel': 'rbf', 'gamma': 0.0003981071705534969}
0.617 (+/-0.238) for {'C': 26.061535499988945, 'kernel': 'rbf', 'gamma': 0.003981071705534969}
0.616 (+/-0.237) for {'C': 26.061535499988945, 'kernel': 'rbf', 'gamma': 0.03981071705534969}
0.613 (+/-0.240) for {'C': 26.061535499988945, 'kernel': 'rbf', 'gamma': 0.3981071705534969}
0.499 (+/-0.002) for {'C': 26.061535499988945, 'kernel': 'rbf', 'gamma': 3.981071705534969}
0.500 (+/-0.000) for {'C': 26.061535499988945, 'kernel': 'rbf', 'gamma': 39.81071705534969}
0.500 (+/-0.000) for {'C': 26.061535499988945, 'kernel': 'rbf', 'gamma': 398.1071705534969}
0.500 (+/-0.000) for {'C': 26.061535499988945, 'kernel': 'rbf', 'gamma': 3981.0717055349733}
0.500 (+/-0.000) for {'C': 26.54605561975539, 'kernel': 'rbf', 'gamma': 3.981071705534969e-07}
0.500 (+/-0.000) for {'C': 26.54605561975539, 'kernel': 'rbf', 'gamma': 3.981071705534969e-06}
0.500 (+/-0.000) for {'C': 26.54605561975539, 'kernel': 'rbf', 'gamma': 3.9810717055349695e-05}
0.500 (+/-0.000) for {'C': 26.54605561975539, 'kernel': 'rbf', 'gamma': 0.0003981071705534969}
0.617 (+/-0.238) for {'C': 26.54605561975539, 'kernel': 'rbf', 'gamma': 0.003981071705534969}
0.616 (+/-0.237) for {'C': 26.54605561975539, 'kernel': 'rbf', 'gamma': 0.03981071705534969}
0.614 (+/-0.240) for {'C': 26.54605561975539, 'kernel': 'rbf', 'gamma': 0.3981071705534969}
0.499 (+/-0.002) for {'C': 26.54605561975539, 'kernel': 'rbf', 'gamma': 3.981071705534969}
0.500 (+/-0.000) for {'C': 26.54605561975539, 'kernel': 'rbf', 'gamma': 39.81071705534969}
0.500 (+/-0.000) for {'C': 26.54605561975539, 'kernel': 'rbf', 'gamma': 398.1071705534969}
0.500 (+/-0.000) for {'C': 26.54605561975539, 'kernel': 'rbf', 'gamma': 3981.0717055349733}
0.500 (+/-0.000) for {'C': 27.039583641088424, 'kernel': 'rbf', 'gamma': 3.981071705534969e-07}
0.500 (+/-0.000) for {'C': 27.039583641088424, 'kernel': 'rbf', 'gamma': 3.981071705534969e-06}
0.500 (+/-0.000) for {'C': 27.039583641088424, 'kernel': 'rbf', 'gamma': 3.9810717055349695e-05}
0.500 (+/-0.000) for {'C': 27.039583641088424, 'kernel': 'rbf', 'gamma': 0.0003981071705534969}
0.617 (+/-0.238) for {'C': 27.039583641088424, 'kernel': 'rbf', 'gamma': 0.003981071705534969}
0.616 (+/-0.237) for {'C': 27.039583641088424, 'kernel': 'rbf', 'gamma': 0.03981071705534969}
0.614 (+/-0.240) for {'C': 27.039583641088424, 'kernel': 'rbf', 'gamma': 0.3981071705534969}
0.499 (+/-0.002) for {'C': 27.039583641088424, 'kernel': 'rbf', 'gamma': 3.981071705534969}
0.500 (+/-0.000) for {'C': 27.039583641088424, 'kernel': 'rbf', 'gamma': 39.81071705534969}
0.500 (+/-0.000) for {'C': 27.039583641088424, 'kernel': 'rbf', 'gamma': 398.1071705534969}
0.500 (+/-0.000) for {'C': 27.039583641088424, 'kernel': 'rbf', 'gamma': 3981.0717055349733}
0.500 (+/-0.000) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 3.981071705534969e-07}
0.500 (+/-0.000) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 3.981071705534969e-06}
0.500 (+/-0.000) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 3.9810717055349695e-05}
0.500 (+/-0.000) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 0.0003981071705534969}
0.617 (+/-0.238) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 0.003981071705534969}
0.616 (+/-0.237) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 0.03981071705534969}
0.614 (+/-0.239) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 0.3981071705534969}
0.499 (+/-0.002) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 3.981071705534969}
0.500 (+/-0.000) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 39.81071705534969}
0.500 (+/-0.000) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 398.1071705534969}
0.500 (+/-0.000) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 3981.0717055349733}
0.500 (+/-0.000) for {'C': 28.054336379517135, 'kernel': 'rbf', 'gamma': 3.981071705534969e-07}
0.500 (+/-0.000) for {'C': 28.054336379517135, 'kernel': 'rbf', 'gamma': 3.981071705534969e-06}
0.500 (+/-0.000) for {'C': 28.054336379517135, 'kernel': 'rbf', 'gamma': 3.9810717055349695e-05}
0.500 (+/-0.000) for {'C': 28.054336379517135, 'kernel': 'rbf', 'gamma': 0.0003981071705534969}
0.617 (+/-0.238) for {'C': 28.054336379517135, 'kernel': 'rbf', 'gamma': 0.003981071705534969}
0.616 (+/-0.237) for {'C': 28.054336379517135, 'kernel': 'rbf', 'gamma': 0.03981071705534969}
0.614 (+/-0.239) for {'C': 28.054336379517135, 'kernel': 'rbf', 'gamma': 0.3981071705534969}
0.499 (+/-0.002) for {'C': 28.054336379517135, 'kernel': 'rbf', 'gamma': 3.981071705534969}
0.500 (+/-0.000) for {'C': 28.054336379517135, 'kernel': 'rbf', 'gamma': 39.81071705534969}
0.500 (+/-0.000) for {'C': 28.054336379517135, 'kernel': 'rbf', 'gamma': 398.1071705534969}
0.500 (+/-0.000) for {'C': 28.054336379517135, 'kernel': 'rbf', 'gamma': 3981.0717055349733}
0.500 (+/-0.000) for {'C': 28.57590543374946, 'kernel': 'rbf', 'gamma': 3.981071705534969e-07}
0.500 (+/-0.000) for {'C': 28.57590543374946, 'kernel': 'rbf', 'gamma': 3.981071705534969e-06}
0.500 (+/-0.000) for {'C': 28.57590543374946, 'kernel': 'rbf', 'gamma': 3.9810717055349695e-05}
0.500 (+/-0.000) for {'C': 28.57590543374946, 'kernel': 'rbf', 'gamma': 0.0003981071705534969}
0.617 (+/-0.238) for {'C': 28.57590543374946, 'kernel': 'rbf', 'gamma': 0.003981071705534969}
0.616 (+/-0.237) for {'C': 28.57590543374946, 'kernel': 'rbf', 'gamma': 0.03981071705534969}
0.614 (+/-0.239) for {'C': 28.57590543374946, 'kernel': 'rbf', 'gamma': 0.3981071705534969}
0.499 (+/-0.002) for {'C': 28.57590543374946, 'kernel': 'rbf', 'gamma': 3.981071705534969}
0.500 (+/-0.000) for {'C': 28.57590543374946, 'kernel': 'rbf', 'gamma': 39.81071705534969}
0.500 (+/-0.000) for {'C': 28.57590543374946, 'kernel': 'rbf', 'gamma': 398.1071705534969}
0.500 (+/-0.000) for {'C': 28.57590543374946, 'kernel': 'rbf', 'gamma': 3981.0717055349733}
0.500 (+/-0.000) for {'C': 29.10717118066605, 'kernel': 'rbf', 'gamma': 3.981071705534969e-07}
0.500 (+/-0.000) for {'C': 29.10717118066605, 'kernel': 'rbf', 'gamma': 3.981071705534969e-06}
0.500 (+/-0.000) for {'C': 29.10717118066605, 'kernel': 'rbf', 'gamma': 3.9810717055349695e-05}
0.500 (+/-0.000) for {'C': 29.10717118066605, 'kernel': 'rbf', 'gamma': 0.0003981071705534969}
0.617 (+/-0.238) for {'C': 29.10717118066605, 'kernel': 'rbf', 'gamma': 0.003981071705534969}
0.616 (+/-0.237) for {'C': 29.10717118066605, 'kernel': 'rbf', 'gamma': 0.03981071705534969}
0.614 (+/-0.239) for {'C': 29.10717118066605, 'kernel': 'rbf', 'gamma': 0.3981071705534969}
0.499 (+/-0.002) for {'C': 29.10717118066605, 'kernel': 'rbf', 'gamma': 3.981071705534969}
0.500 (+/-0.000) for {'C': 29.10717118066605, 'kernel': 'rbf', 'gamma': 39.81071705534969}
0.500 (+/-0.000) for {'C': 29.10717118066605, 'kernel': 'rbf', 'gamma': 398.1071705534969}
0.500 (+/-0.000) for {'C': 29.10717118066605, 'kernel': 'rbf', 'gamma': 3981.0717055349733}
0.500 (+/-0.000) for {'C': 29.648313895243408, 'kernel': 'rbf', 'gamma': 3.981071705534969e-07}
0.500 (+/-0.000) for {'C': 29.648313895243408, 'kernel': 'rbf', 'gamma': 3.981071705534969e-06}
0.500 (+/-0.000) for {'C': 29.648313895243408, 'kernel': 'rbf', 'gamma': 3.9810717055349695e-05}
0.500 (+/-0.000) for {'C': 29.648313895243408, 'kernel': 'rbf', 'gamma': 0.0003981071705534969}
0.617 (+/-0.238) for {'C': 29.648313895243408, 'kernel': 'rbf', 'gamma': 0.003981071705534969}
0.616 (+/-0.237) for {'C': 29.648313895243408, 'kernel': 'rbf', 'gamma': 0.03981071705534969}
0.614 (+/-0.239) for {'C': 29.648313895243408, 'kernel': 'rbf', 'gamma': 0.3981071705534969}
0.499 (+/-0.002) for {'C': 29.648313895243408, 'kernel': 'rbf', 'gamma': 3.981071705534969}
0.500 (+/-0.000) for {'C': 29.648313895243408, 'kernel': 'rbf', 'gamma': 39.81071705534969}
0.500 (+/-0.000) for {'C': 29.648313895243408, 'kernel': 'rbf', 'gamma': 398.1071705534969}
0.500 (+/-0.000) for {'C': 29.648313895243408, 'kernel': 'rbf', 'gamma': 3981.0717055349733}
0.500 (+/-0.000) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 3.981071705534969e-07}
0.500 (+/-0.000) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 3.981071705534969e-06}
0.500 (+/-0.000) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 3.9810717055349695e-05}
0.500 (+/-0.000) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 0.0003981071705534969}
0.617 (+/-0.238) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 0.003981071705534969}
0.616 (+/-0.237) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 0.03981071705534969}
0.614 (+/-0.239) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 0.3981071705534969}
0.499 (+/-0.002) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 3.981071705534969}
0.500 (+/-0.000) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 39.81071705534969}
0.500 (+/-0.000) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 398.1071705534969}
0.500 (+/-0.000) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 3981.0717055349733}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'linear'}
0.616 (+/-0.238) for {'C': 10.0, 'kernel': 'linear'}
0.613 (+/-0.242) for {'C': 100.0, 'kernel': 'linear'}
0.613 (+/-0.241) for {'C': 1000.0, 'kernel': 'linear'}
0.613 (+/-0.241) for {'C': 10000.0, 'kernel': 'linear'}
0.613 (+/-0.241) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.003981071705534969}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6166666666666667
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.496 (+/-0.001) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 3.981071705534969e-07}
0.496 (+/-0.001) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 3.981071705534969e-06}
0.496 (+/-0.001) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 3.9810717055349695e-05}
0.747 (+/-0.502) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.0003981071705534969}
0.747 (+/-0.502) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.003981071705534969}
0.689 (+/-0.374) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.03981071705534969}
0.626 (+/-0.318) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.3981071705534969}
0.496 (+/-0.001) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 3.981071705534969}
0.496 (+/-0.001) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 39.81071705534969}
0.496 (+/-0.001) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 398.1071705534969}
0.496 (+/-0.001) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 3981.0717055349733}
0.496 (+/-0.001) for {'C': 25.58585886905646, 'kernel': 'rbf', 'gamma': 3.981071705534969e-07}
0.496 (+/-0.001) for {'C': 25.58585886905646, 'kernel': 'rbf', 'gamma': 3.981071705534969e-06}
0.496 (+/-0.001) for {'C': 25.58585886905646, 'kernel': 'rbf', 'gamma': 3.9810717055349695e-05}
0.747 (+/-0.502) for {'C': 25.58585886905646, 'kernel': 'rbf', 'gamma': 0.0003981071705534969}
0.747 (+/-0.502) for {'C': 25.58585886905646, 'kernel': 'rbf', 'gamma': 0.003981071705534969}
0.689 (+/-0.374) for {'C': 25.58585886905646, 'kernel': 'rbf', 'gamma': 0.03981071705534969}
0.626 (+/-0.318) for {'C': 25.58585886905646, 'kernel': 'rbf', 'gamma': 0.3981071705534969}
0.496 (+/-0.001) for {'C': 25.58585886905646, 'kernel': 'rbf', 'gamma': 3.981071705534969}
0.496 (+/-0.001) for {'C': 25.58585886905646, 'kernel': 'rbf', 'gamma': 39.81071705534969}
0.496 (+/-0.001) for {'C': 25.58585886905646, 'kernel': 'rbf', 'gamma': 398.1071705534969}
0.496 (+/-0.001) for {'C': 25.58585886905646, 'kernel': 'rbf', 'gamma': 3981.0717055349733}
0.496 (+/-0.001) for {'C': 26.061535499988945, 'kernel': 'rbf', 'gamma': 3.981071705534969e-07}
0.496 (+/-0.001) for {'C': 26.061535499988945, 'kernel': 'rbf', 'gamma': 3.981071705534969e-06}
0.496 (+/-0.001) for {'C': 26.061535499988945, 'kernel': 'rbf', 'gamma': 3.9810717055349695e-05}
0.747 (+/-0.502) for {'C': 26.061535499988945, 'kernel': 'rbf', 'gamma': 0.0003981071705534969}
0.747 (+/-0.502) for {'C': 26.061535499988945, 'kernel': 'rbf', 'gamma': 0.003981071705534969}
0.689 (+/-0.374) for {'C': 26.061535499988945, 'kernel': 'rbf', 'gamma': 0.03981071705534969}
0.626 (+/-0.318) for {'C': 26.061535499988945, 'kernel': 'rbf', 'gamma': 0.3981071705534969}
0.496 (+/-0.001) for {'C': 26.061535499988945, 'kernel': 'rbf', 'gamma': 3.981071705534969}
0.496 (+/-0.001) for {'C': 26.061535499988945, 'kernel': 'rbf', 'gamma': 39.81071705534969}
0.496 (+/-0.001) for {'C': 26.061535499988945, 'kernel': 'rbf', 'gamma': 398.1071705534969}
0.496 (+/-0.001) for {'C': 26.061535499988945, 'kernel': 'rbf', 'gamma': 3981.0717055349733}
0.496 (+/-0.001) for {'C': 26.54605561975539, 'kernel': 'rbf', 'gamma': 3.981071705534969e-07}
0.496 (+/-0.001) for {'C': 26.54605561975539, 'kernel': 'rbf', 'gamma': 3.981071705534969e-06}
0.496 (+/-0.001) for {'C': 26.54605561975539, 'kernel': 'rbf', 'gamma': 3.9810717055349695e-05}
0.747 (+/-0.502) for {'C': 26.54605561975539, 'kernel': 'rbf', 'gamma': 0.0003981071705534969}
0.747 (+/-0.502) for {'C': 26.54605561975539, 'kernel': 'rbf', 'gamma': 0.003981071705534969}
0.689 (+/-0.374) for {'C': 26.54605561975539, 'kernel': 'rbf', 'gamma': 0.03981071705534969}
0.631 (+/-0.319) for {'C': 26.54605561975539, 'kernel': 'rbf', 'gamma': 0.3981071705534969}
0.496 (+/-0.001) for {'C': 26.54605561975539, 'kernel': 'rbf', 'gamma': 3.981071705534969}
0.496 (+/-0.001) for {'C': 26.54605561975539, 'kernel': 'rbf', 'gamma': 39.81071705534969}
0.496 (+/-0.001) for {'C': 26.54605561975539, 'kernel': 'rbf', 'gamma': 398.1071705534969}
0.496 (+/-0.001) for {'C': 26.54605561975539, 'kernel': 'rbf', 'gamma': 3981.0717055349733}
0.496 (+/-0.001) for {'C': 27.039583641088424, 'kernel': 'rbf', 'gamma': 3.981071705534969e-07}
0.496 (+/-0.001) for {'C': 27.039583641088424, 'kernel': 'rbf', 'gamma': 3.981071705534969e-06}
0.496 (+/-0.001) for {'C': 27.039583641088424, 'kernel': 'rbf', 'gamma': 3.9810717055349695e-05}
0.747 (+/-0.502) for {'C': 27.039583641088424, 'kernel': 'rbf', 'gamma': 0.0003981071705534969}
0.747 (+/-0.502) for {'C': 27.039583641088424, 'kernel': 'rbf', 'gamma': 0.003981071705534969}
0.698 (+/-0.383) for {'C': 27.039583641088424, 'kernel': 'rbf', 'gamma': 0.03981071705534969}
0.631 (+/-0.319) for {'C': 27.039583641088424, 'kernel': 'rbf', 'gamma': 0.3981071705534969}
0.496 (+/-0.001) for {'C': 27.039583641088424, 'kernel': 'rbf', 'gamma': 3.981071705534969}
0.496 (+/-0.001) for {'C': 27.039583641088424, 'kernel': 'rbf', 'gamma': 39.81071705534969}
0.496 (+/-0.001) for {'C': 27.039583641088424, 'kernel': 'rbf', 'gamma': 398.1071705534969}
0.496 (+/-0.001) for {'C': 27.039583641088424, 'kernel': 'rbf', 'gamma': 3981.0717055349733}
0.496 (+/-0.001) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 3.981071705534969e-07}
0.496 (+/-0.001) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 3.981071705534969e-06}
0.496 (+/-0.001) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 3.9810717055349695e-05}
0.747 (+/-0.502) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 0.0003981071705534969}
0.747 (+/-0.502) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 0.003981071705534969}
0.698 (+/-0.383) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 0.03981071705534969}
0.631 (+/-0.319) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 0.3981071705534969}
0.496 (+/-0.001) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 3.981071705534969}
0.496 (+/-0.001) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 39.81071705534969}
0.496 (+/-0.001) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 398.1071705534969}
0.496 (+/-0.001) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 3981.0717055349733}
0.496 (+/-0.001) for {'C': 28.054336379517135, 'kernel': 'rbf', 'gamma': 3.981071705534969e-07}
0.496 (+/-0.001) for {'C': 28.054336379517135, 'kernel': 'rbf', 'gamma': 3.981071705534969e-06}
0.496 (+/-0.001) for {'C': 28.054336379517135, 'kernel': 'rbf', 'gamma': 3.9810717055349695e-05}
0.747 (+/-0.502) for {'C': 28.054336379517135, 'kernel': 'rbf', 'gamma': 0.0003981071705534969}
0.747 (+/-0.502) for {'C': 28.054336379517135, 'kernel': 'rbf', 'gamma': 0.003981071705534969}
0.698 (+/-0.383) for {'C': 28.054336379517135, 'kernel': 'rbf', 'gamma': 0.03981071705534969}
0.631 (+/-0.319) for {'C': 28.054336379517135, 'kernel': 'rbf', 'gamma': 0.3981071705534969}
0.496 (+/-0.001) for {'C': 28.054336379517135, 'kernel': 'rbf', 'gamma': 3.981071705534969}
0.496 (+/-0.001) for {'C': 28.054336379517135, 'kernel': 'rbf', 'gamma': 39.81071705534969}
0.496 (+/-0.001) for {'C': 28.054336379517135, 'kernel': 'rbf', 'gamma': 398.1071705534969}
0.496 (+/-0.001) for {'C': 28.054336379517135, 'kernel': 'rbf', 'gamma': 3981.0717055349733}
0.496 (+/-0.001) for {'C': 28.57590543374946, 'kernel': 'rbf', 'gamma': 3.981071705534969e-07}
0.496 (+/-0.001) for {'C': 28.57590543374946, 'kernel': 'rbf', 'gamma': 3.981071705534969e-06}
0.496 (+/-0.001) for {'C': 28.57590543374946, 'kernel': 'rbf', 'gamma': 3.9810717055349695e-05}
0.747 (+/-0.502) for {'C': 28.57590543374946, 'kernel': 'rbf', 'gamma': 0.0003981071705534969}
0.747 (+/-0.502) for {'C': 28.57590543374946, 'kernel': 'rbf', 'gamma': 0.003981071705534969}
0.698 (+/-0.383) for {'C': 28.57590543374946, 'kernel': 'rbf', 'gamma': 0.03981071705534969}
0.631 (+/-0.319) for {'C': 28.57590543374946, 'kernel': 'rbf', 'gamma': 0.3981071705534969}
0.496 (+/-0.001) for {'C': 28.57590543374946, 'kernel': 'rbf', 'gamma': 3.981071705534969}
0.496 (+/-0.001) for {'C': 28.57590543374946, 'kernel': 'rbf', 'gamma': 39.81071705534969}
0.496 (+/-0.001) for {'C': 28.57590543374946, 'kernel': 'rbf', 'gamma': 398.1071705534969}
0.496 (+/-0.001) for {'C': 28.57590543374946, 'kernel': 'rbf', 'gamma': 3981.0717055349733}
0.496 (+/-0.001) for {'C': 29.10717118066605, 'kernel': 'rbf', 'gamma': 3.981071705534969e-07}
0.496 (+/-0.001) for {'C': 29.10717118066605, 'kernel': 'rbf', 'gamma': 3.981071705534969e-06}
0.496 (+/-0.001) for {'C': 29.10717118066605, 'kernel': 'rbf', 'gamma': 3.9810717055349695e-05}
0.747 (+/-0.502) for {'C': 29.10717118066605, 'kernel': 'rbf', 'gamma': 0.0003981071705534969}
0.747 (+/-0.502) for {'C': 29.10717118066605, 'kernel': 'rbf', 'gamma': 0.003981071705534969}
0.698 (+/-0.383) for {'C': 29.10717118066605, 'kernel': 'rbf', 'gamma': 0.03981071705534969}
0.631 (+/-0.319) for {'C': 29.10717118066605, 'kernel': 'rbf', 'gamma': 0.3981071705534969}
0.496 (+/-0.001) for {'C': 29.10717118066605, 'kernel': 'rbf', 'gamma': 3.981071705534969}
0.496 (+/-0.001) for {'C': 29.10717118066605, 'kernel': 'rbf', 'gamma': 39.81071705534969}
0.496 (+/-0.001) for {'C': 29.10717118066605, 'kernel': 'rbf', 'gamma': 398.1071705534969}
0.496 (+/-0.001) for {'C': 29.10717118066605, 'kernel': 'rbf', 'gamma': 3981.0717055349733}
0.496 (+/-0.001) for {'C': 29.648313895243408, 'kernel': 'rbf', 'gamma': 3.981071705534969e-07}
0.496 (+/-0.001) for {'C': 29.648313895243408, 'kernel': 'rbf', 'gamma': 3.981071705534969e-06}
0.496 (+/-0.001) for {'C': 29.648313895243408, 'kernel': 'rbf', 'gamma': 3.9810717055349695e-05}
0.747 (+/-0.502) for {'C': 29.648313895243408, 'kernel': 'rbf', 'gamma': 0.0003981071705534969}
0.747 (+/-0.502) for {'C': 29.648313895243408, 'kernel': 'rbf', 'gamma': 0.003981071705534969}
0.698 (+/-0.383) for {'C': 29.648313895243408, 'kernel': 'rbf', 'gamma': 0.03981071705534969}
0.631 (+/-0.319) for {'C': 29.648313895243408, 'kernel': 'rbf', 'gamma': 0.3981071705534969}
0.496 (+/-0.001) for {'C': 29.648313895243408, 'kernel': 'rbf', 'gamma': 3.981071705534969}
0.496 (+/-0.001) for {'C': 29.648313895243408, 'kernel': 'rbf', 'gamma': 39.81071705534969}
0.496 (+/-0.001) for {'C': 29.648313895243408, 'kernel': 'rbf', 'gamma': 398.1071705534969}
0.496 (+/-0.001) for {'C': 29.648313895243408, 'kernel': 'rbf', 'gamma': 3981.0717055349733}
0.496 (+/-0.001) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 3.981071705534969e-07}
0.496 (+/-0.001) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 3.981071705534969e-06}
0.496 (+/-0.001) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 3.9810717055349695e-05}
0.747 (+/-0.502) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 0.0003981071705534969}
0.747 (+/-0.502) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 0.003981071705534969}
0.698 (+/-0.383) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 0.03981071705534969}
0.631 (+/-0.319) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 0.3981071705534969}
0.496 (+/-0.001) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 3.981071705534969}
0.496 (+/-0.001) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 39.81071705534969}
0.496 (+/-0.001) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 398.1071705534969}
0.496 (+/-0.001) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 3981.0717055349733}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.722 (+/-0.417) for {'C': 1.0, 'kernel': 'linear'}
0.654 (+/-0.379) for {'C': 10.0, 'kernel': 'linear'}
0.656 (+/-0.384) for {'C': 100.0, 'kernel': 'linear'}
0.651 (+/-0.385) for {'C': 1000.0, 'kernel': 'linear'}
0.651 (+/-0.385) for {'C': 10000.0, 'kernel': 'linear'}
0.651 (+/-0.385) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [1]
检查交叉验证集中测试集标签是否有预测不到的值: {-1}
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 1.00 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.500 (+/-0.000) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 3.981071705534969e-07}
0.500 (+/-0.000) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 3.981071705534969e-06}
0.500 (+/-0.000) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 3.9810717055349695e-05}
0.617 (+/-0.238) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.0003981071705534969}
0.617 (+/-0.238) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.003981071705534969}
0.641 (+/-0.235) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.03981071705534969}
0.613 (+/-0.240) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.3981071705534969}
0.499 (+/-0.002) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 3.981071705534969}
0.500 (+/-0.000) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 39.81071705534969}
0.500 (+/-0.000) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 398.1071705534969}
0.500 (+/-0.000) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 3981.0717055349733}
0.500 (+/-0.000) for {'C': 25.58585886905646, 'kernel': 'rbf', 'gamma': 3.981071705534969e-07}
0.500 (+/-0.000) for {'C': 25.58585886905646, 'kernel': 'rbf', 'gamma': 3.981071705534969e-06}
0.500 (+/-0.000) for {'C': 25.58585886905646, 'kernel': 'rbf', 'gamma': 3.9810717055349695e-05}
0.617 (+/-0.238) for {'C': 25.58585886905646, 'kernel': 'rbf', 'gamma': 0.0003981071705534969}
0.617 (+/-0.238) for {'C': 25.58585886905646, 'kernel': 'rbf', 'gamma': 0.003981071705534969}
0.641 (+/-0.235) for {'C': 25.58585886905646, 'kernel': 'rbf', 'gamma': 0.03981071705534969}
0.613 (+/-0.240) for {'C': 25.58585886905646, 'kernel': 'rbf', 'gamma': 0.3981071705534969}
0.499 (+/-0.002) for {'C': 25.58585886905646, 'kernel': 'rbf', 'gamma': 3.981071705534969}
0.500 (+/-0.000) for {'C': 25.58585886905646, 'kernel': 'rbf', 'gamma': 39.81071705534969}
0.500 (+/-0.000) for {'C': 25.58585886905646, 'kernel': 'rbf', 'gamma': 398.1071705534969}
0.500 (+/-0.000) for {'C': 25.58585886905646, 'kernel': 'rbf', 'gamma': 3981.0717055349733}
0.500 (+/-0.000) for {'C': 26.061535499988945, 'kernel': 'rbf', 'gamma': 3.981071705534969e-07}
0.500 (+/-0.000) for {'C': 26.061535499988945, 'kernel': 'rbf', 'gamma': 3.981071705534969e-06}
0.500 (+/-0.000) for {'C': 26.061535499988945, 'kernel': 'rbf', 'gamma': 3.9810717055349695e-05}
0.617 (+/-0.238) for {'C': 26.061535499988945, 'kernel': 'rbf', 'gamma': 0.0003981071705534969}
0.617 (+/-0.238) for {'C': 26.061535499988945, 'kernel': 'rbf', 'gamma': 0.003981071705534969}
0.641 (+/-0.235) for {'C': 26.061535499988945, 'kernel': 'rbf', 'gamma': 0.03981071705534969}
0.613 (+/-0.240) for {'C': 26.061535499988945, 'kernel': 'rbf', 'gamma': 0.3981071705534969}
0.499 (+/-0.002) for {'C': 26.061535499988945, 'kernel': 'rbf', 'gamma': 3.981071705534969}
0.500 (+/-0.000) for {'C': 26.061535499988945, 'kernel': 'rbf', 'gamma': 39.81071705534969}
0.500 (+/-0.000) for {'C': 26.061535499988945, 'kernel': 'rbf', 'gamma': 398.1071705534969}
0.500 (+/-0.000) for {'C': 26.061535499988945, 'kernel': 'rbf', 'gamma': 3981.0717055349733}
0.500 (+/-0.000) for {'C': 26.54605561975539, 'kernel': 'rbf', 'gamma': 3.981071705534969e-07}
0.500 (+/-0.000) for {'C': 26.54605561975539, 'kernel': 'rbf', 'gamma': 3.981071705534969e-06}
0.500 (+/-0.000) for {'C': 26.54605561975539, 'kernel': 'rbf', 'gamma': 3.9810717055349695e-05}
0.617 (+/-0.238) for {'C': 26.54605561975539, 'kernel': 'rbf', 'gamma': 0.0003981071705534969}
0.617 (+/-0.238) for {'C': 26.54605561975539, 'kernel': 'rbf', 'gamma': 0.003981071705534969}
0.641 (+/-0.235) for {'C': 26.54605561975539, 'kernel': 'rbf', 'gamma': 0.03981071705534969}
0.614 (+/-0.240) for {'C': 26.54605561975539, 'kernel': 'rbf', 'gamma': 0.3981071705534969}
0.499 (+/-0.002) for {'C': 26.54605561975539, 'kernel': 'rbf', 'gamma': 3.981071705534969}
0.500 (+/-0.000) for {'C': 26.54605561975539, 'kernel': 'rbf', 'gamma': 39.81071705534969}
0.500 (+/-0.000) for {'C': 26.54605561975539, 'kernel': 'rbf', 'gamma': 398.1071705534969}
0.500 (+/-0.000) for {'C': 26.54605561975539, 'kernel': 'rbf', 'gamma': 3981.0717055349733}
0.500 (+/-0.000) for {'C': 27.039583641088424, 'kernel': 'rbf', 'gamma': 3.981071705534969e-07}
0.500 (+/-0.000) for {'C': 27.039583641088424, 'kernel': 'rbf', 'gamma': 3.981071705534969e-06}
0.500 (+/-0.000) for {'C': 27.039583641088424, 'kernel': 'rbf', 'gamma': 3.9810717055349695e-05}
0.617 (+/-0.238) for {'C': 27.039583641088424, 'kernel': 'rbf', 'gamma': 0.0003981071705534969}
0.617 (+/-0.238) for {'C': 27.039583641088424, 'kernel': 'rbf', 'gamma': 0.003981071705534969}
0.666 (+/-0.315) for {'C': 27.039583641088424, 'kernel': 'rbf', 'gamma': 0.03981071705534969}
0.614 (+/-0.240) for {'C': 27.039583641088424, 'kernel': 'rbf', 'gamma': 0.3981071705534969}
0.499 (+/-0.002) for {'C': 27.039583641088424, 'kernel': 'rbf', 'gamma': 3.981071705534969}
0.500 (+/-0.000) for {'C': 27.039583641088424, 'kernel': 'rbf', 'gamma': 39.81071705534969}
0.500 (+/-0.000) for {'C': 27.039583641088424, 'kernel': 'rbf', 'gamma': 398.1071705534969}
0.500 (+/-0.000) for {'C': 27.039583641088424, 'kernel': 'rbf', 'gamma': 3981.0717055349733}
0.500 (+/-0.000) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 3.981071705534969e-07}
0.500 (+/-0.000) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 3.981071705534969e-06}
0.500 (+/-0.000) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 3.9810717055349695e-05}
0.617 (+/-0.238) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 0.0003981071705534969}
0.617 (+/-0.238) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 0.003981071705534969}
0.666 (+/-0.315) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 0.03981071705534969}
0.614 (+/-0.239) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 0.3981071705534969}
0.499 (+/-0.002) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 3.981071705534969}
0.500 (+/-0.000) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 39.81071705534969}
0.500 (+/-0.000) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 398.1071705534969}
0.500 (+/-0.000) for {'C': 27.54228703338166, 'kernel': 'rbf', 'gamma': 3981.0717055349733}
0.500 (+/-0.000) for {'C': 28.054336379517135, 'kernel': 'rbf', 'gamma': 3.981071705534969e-07}
0.500 (+/-0.000) for {'C': 28.054336379517135, 'kernel': 'rbf', 'gamma': 3.981071705534969e-06}
0.500 (+/-0.000) for {'C': 28.054336379517135, 'kernel': 'rbf', 'gamma': 3.9810717055349695e-05}
0.617 (+/-0.238) for {'C': 28.054336379517135, 'kernel': 'rbf', 'gamma': 0.0003981071705534969}
0.617 (+/-0.238) for {'C': 28.054336379517135, 'kernel': 'rbf', 'gamma': 0.003981071705534969}
0.666 (+/-0.315) for {'C': 28.054336379517135, 'kernel': 'rbf', 'gamma': 0.03981071705534969}
0.614 (+/-0.239) for {'C': 28.054336379517135, 'kernel': 'rbf', 'gamma': 0.3981071705534969}
0.499 (+/-0.002) for {'C': 28.054336379517135, 'kernel': 'rbf', 'gamma': 3.981071705534969}
0.500 (+/-0.000) for {'C': 28.054336379517135, 'kernel': 'rbf', 'gamma': 39.81071705534969}
0.500 (+/-0.000) for {'C': 28.054336379517135, 'kernel': 'rbf', 'gamma': 398.1071705534969}
0.500 (+/-0.000) for {'C': 28.054336379517135, 'kernel': 'rbf', 'gamma': 3981.0717055349733}
0.500 (+/-0.000) for {'C': 28.57590543374946, 'kernel': 'rbf', 'gamma': 3.981071705534969e-07}
0.500 (+/-0.000) for {'C': 28.57590543374946, 'kernel': 'rbf', 'gamma': 3.981071705534969e-06}
0.500 (+/-0.000) for {'C': 28.57590543374946, 'kernel': 'rbf', 'gamma': 3.9810717055349695e-05}
0.617 (+/-0.238) for {'C': 28.57590543374946, 'kernel': 'rbf', 'gamma': 0.0003981071705534969}
0.617 (+/-0.238) for {'C': 28.57590543374946, 'kernel': 'rbf', 'gamma': 0.003981071705534969}
0.666 (+/-0.315) for {'C': 28.57590543374946, 'kernel': 'rbf', 'gamma': 0.03981071705534969}
0.614 (+/-0.239) for {'C': 28.57590543374946, 'kernel': 'rbf', 'gamma': 0.3981071705534969}
0.499 (+/-0.002) for {'C': 28.57590543374946, 'kernel': 'rbf', 'gamma': 3.981071705534969}
0.500 (+/-0.000) for {'C': 28.57590543374946, 'kernel': 'rbf', 'gamma': 39.81071705534969}
0.500 (+/-0.000) for {'C': 28.57590543374946, 'kernel': 'rbf', 'gamma': 398.1071705534969}
0.500 (+/-0.000) for {'C': 28.57590543374946, 'kernel': 'rbf', 'gamma': 3981.0717055349733}
0.500 (+/-0.000) for {'C': 29.10717118066605, 'kernel': 'rbf', 'gamma': 3.981071705534969e-07}
0.500 (+/-0.000) for {'C': 29.10717118066605, 'kernel': 'rbf', 'gamma': 3.981071705534969e-06}
0.500 (+/-0.000) for {'C': 29.10717118066605, 'kernel': 'rbf', 'gamma': 3.9810717055349695e-05}
0.617 (+/-0.238) for {'C': 29.10717118066605, 'kernel': 'rbf', 'gamma': 0.0003981071705534969}
0.617 (+/-0.238) for {'C': 29.10717118066605, 'kernel': 'rbf', 'gamma': 0.003981071705534969}
0.666 (+/-0.315) for {'C': 29.10717118066605, 'kernel': 'rbf', 'gamma': 0.03981071705534969}
0.614 (+/-0.239) for {'C': 29.10717118066605, 'kernel': 'rbf', 'gamma': 0.3981071705534969}
0.499 (+/-0.002) for {'C': 29.10717118066605, 'kernel': 'rbf', 'gamma': 3.981071705534969}
0.500 (+/-0.000) for {'C': 29.10717118066605, 'kernel': 'rbf', 'gamma': 39.81071705534969}
0.500 (+/-0.000) for {'C': 29.10717118066605, 'kernel': 'rbf', 'gamma': 398.1071705534969}
0.500 (+/-0.000) for {'C': 29.10717118066605, 'kernel': 'rbf', 'gamma': 3981.0717055349733}
0.500 (+/-0.000) for {'C': 29.648313895243408, 'kernel': 'rbf', 'gamma': 3.981071705534969e-07}
0.500 (+/-0.000) for {'C': 29.648313895243408, 'kernel': 'rbf', 'gamma': 3.981071705534969e-06}
0.500 (+/-0.000) for {'C': 29.648313895243408, 'kernel': 'rbf', 'gamma': 3.9810717055349695e-05}
0.617 (+/-0.238) for {'C': 29.648313895243408, 'kernel': 'rbf', 'gamma': 0.0003981071705534969}
0.617 (+/-0.238) for {'C': 29.648313895243408, 'kernel': 'rbf', 'gamma': 0.003981071705534969}
0.666 (+/-0.315) for {'C': 29.648313895243408, 'kernel': 'rbf', 'gamma': 0.03981071705534969}
0.614 (+/-0.239) for {'C': 29.648313895243408, 'kernel': 'rbf', 'gamma': 0.3981071705534969}
0.499 (+/-0.002) for {'C': 29.648313895243408, 'kernel': 'rbf', 'gamma': 3.981071705534969}
0.500 (+/-0.000) for {'C': 29.648313895243408, 'kernel': 'rbf', 'gamma': 39.81071705534969}
0.500 (+/-0.000) for {'C': 29.648313895243408, 'kernel': 'rbf', 'gamma': 398.1071705534969}
0.500 (+/-0.000) for {'C': 29.648313895243408, 'kernel': 'rbf', 'gamma': 3981.0717055349733}
0.500 (+/-0.000) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 3.981071705534969e-07}
0.500 (+/-0.000) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 3.981071705534969e-06}
0.500 (+/-0.000) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 3.9810717055349695e-05}
0.617 (+/-0.238) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 0.0003981071705534969}
0.617 (+/-0.238) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 0.003981071705534969}
0.666 (+/-0.315) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 0.03981071705534969}
0.614 (+/-0.239) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 0.3981071705534969}
0.499 (+/-0.002) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 3.981071705534969}
0.500 (+/-0.000) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 39.81071705534969}
0.500 (+/-0.000) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 398.1071705534969}
0.500 (+/-0.000) for {'C': 30.199517204020154, 'kernel': 'rbf', 'gamma': 3981.0717055349733}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.641 (+/-0.235) for {'C': 1.0, 'kernel': 'linear'}
0.660 (+/-0.315) for {'C': 10.0, 'kernel': 'linear'}
0.613 (+/-0.242) for {'C': 100.0, 'kernel': 'linear'}
0.613 (+/-0.241) for {'C': 1000.0, 'kernel': 'linear'}
0.613 (+/-0.241) for {'C': 10000.0, 'kernel': 'linear'}
0.613 (+/-0.241) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 27.039583641088424, 'kernel': 'rbf', 'gamma': 0.03981071705534969}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6658653846153846
第1轮loop中,tunemodel函数得到的最优参数是: ([{'C': array([26.54605562, 26.64403528, 26.74237657, 26.84108084, 26.94014941,
27.03958364, 27.13938488, 27.23955447, 27.34009379, 27.44100418,
27.54228703]), 'kernel': ['rbf'], 'gamma': array([0.00398107, 0.00630957, 0.01 , 0.01584893, 0.02511886,
0.03981072, 0.06309573, 0.1 , 0.15848932, 0.25118864,
0.39810717])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}], {'C': 27.039583641088424, 'kernel': 'rbf', 'gamma': 0.03981071705534969}, 0.6658653846153846)
这是第1次迭代微调C和gamma。
第1次迭代,得到delta: [5.02703392e-01 4.16333634e-17]
SVC模型clf_op_iter的参数是: {'kernel': 'rbf', 'decision_function_shape': 'ovr', 'class_weight': {-1: 15}, 'tol': 0.001, 'gamma': 0.03981071705534969, 'random_state': 0, 'cache_size': 200, 'verbose': False, 'degree': 3, 'C': 27.039583641088424, 'probability': False, 'shrinking': True, 'coef0': 0.0, 'max_iter': -1}
训练模型SVC对训练样本的预测准确率: 0.9468462083628633
测试集中,预测为舞弊样本的有: (array([ 370, 1246, 1247, 1248, 1251, 1252, 1255, 1256], dtype=int64),)
测试集中,实际为舞弊样本的有: (array([1246, 1247, 1248, 1249, 1250, 1251, 1252, 1253, 1254, 1255, 1256],
dtype=int64),)
预测的舞弊样本数目: 8
训练模型SVC对测试样本的预测准确率: 0.9567682494684621
以上是第16次特征筛选。
第16次特征筛选,AUC值是: 0.8177805340726688
X_train_iter_svc.shape is: (1257, 36)
X_test_iter_svc.shape is: (1257, 36)
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.622 (+/-0.336) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.590 (+/-0.230) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6166666666666667
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.723 (+/-0.417) for {'C': 1.0, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.630 (+/-0.310) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.637 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6661858974358974
RBF的粗grid search最佳超参: {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0} RBF的粗grid search最高分值: 0.6166666666666667
linear的粗grid search最佳超参: {'C': 1.0, 'kernel': 'linear'} linear的粗grid search最高分值: 0.6661858974358974
粗grid search得到的parameter是:
[{'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['rbf'], 'gamma': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.629 (+/-0.329) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.622 (+/-0.336) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.372) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.319) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.622 (+/-0.336) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.674 (+/-0.375) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.310) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.622 (+/-0.336) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.674 (+/-0.375) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.630 (+/-0.310) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.310) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.622 (+/-0.336) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'linear'}
0.689 (+/-0.436) for {'C': 10.0, 'kernel': 'linear'}
0.630 (+/-0.310) for {'C': 100.0, 'kernel': 'linear'}
0.630 (+/-0.310) for {'C': 1000.0, 'kernel': 'linear'}
0.630 (+/-0.310) for {'C': 10000.0, 'kernel': 'linear'}
0.630 (+/-0.310) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [1]
检查交叉验证集中测试集标签是否有预测不到的值: {-1}
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 1.00 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.590 (+/-0.230) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.639 (+/-0.236) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.613 (+/-0.241) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.590 (+/-0.230) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.616 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.639 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.637 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.590 (+/-0.230) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.616 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.639 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.637 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.637 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.590 (+/-0.230) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'linear'}
0.616 (+/-0.238) for {'C': 10.0, 'kernel': 'linear'}
0.637 (+/-0.239) for {'C': 100.0, 'kernel': 'linear'}
0.637 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
0.637 (+/-0.238) for {'C': 10000.0, 'kernel': 'linear'}
0.637 (+/-0.238) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.12 0.17 0.14 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6392628205128205
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.664 (+/-0.388) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.629 (+/-0.329) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.383) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.622 (+/-0.336) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.698 (+/-0.383) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.646 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.319) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.622 (+/-0.336) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.698 (+/-0.383) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.654 (+/-0.393) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.310) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.622 (+/-0.336) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.698 (+/-0.383) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.665 (+/-0.381) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.630 (+/-0.310) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.310) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.622 (+/-0.336) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.723 (+/-0.417) for {'C': 1.0, 'kernel': 'linear'}
0.654 (+/-0.379) for {'C': 10.0, 'kernel': 'linear'}
0.630 (+/-0.310) for {'C': 100.0, 'kernel': 'linear'}
0.630 (+/-0.310) for {'C': 1000.0, 'kernel': 'linear'}
0.630 (+/-0.310) for {'C': 10000.0, 'kernel': 'linear'}
0.630 (+/-0.310) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [1]
检查交叉验证集中测试集标签是否有预测不到的值: {-1}
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 1.00 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.315) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.587 (+/-0.235) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.590 (+/-0.230) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.666 (+/-0.315) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.611 (+/-0.243) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.613 (+/-0.241) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.590 (+/-0.230) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.315) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.611 (+/-0.243) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.637 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.590 (+/-0.230) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.666 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.661 (+/-0.316) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.637 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.637 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.590 (+/-0.230) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.659 (+/-0.318) for {'C': 10.0, 'kernel': 'linear'}
0.637 (+/-0.239) for {'C': 100.0, 'kernel': 'linear'}
0.637 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
0.637 (+/-0.238) for {'C': 10000.0, 'kernel': 'linear'}
0.637 (+/-0.238) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6661858974358974
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.629 (+/-0.329) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.622 (+/-0.336) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.372) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.319) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.622 (+/-0.336) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.674 (+/-0.375) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.310) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.622 (+/-0.336) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.674 (+/-0.375) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.630 (+/-0.310) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.310) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.622 (+/-0.336) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.664 (+/-0.388) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.689 (+/-0.436) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.689 (+/-0.436) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [1]
检查交叉验证集中测试集标签是否有预测不到的值: {-1}
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 1.00 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.590 (+/-0.230) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.639 (+/-0.236) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.613 (+/-0.241) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.590 (+/-0.230) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.616 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.639 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.637 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.590 (+/-0.230) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.616 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.639 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.637 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.637 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.590 (+/-0.230) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.616 (+/-0.238) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.12 0.17 0.14 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6392628205128205
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.664 (+/-0.388) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.629 (+/-0.329) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.383) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.622 (+/-0.336) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.698 (+/-0.383) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.646 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.319) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.622 (+/-0.336) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.698 (+/-0.383) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.654 (+/-0.393) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.310) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.622 (+/-0.336) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.698 (+/-0.383) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.665 (+/-0.381) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.630 (+/-0.310) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.310) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.622 (+/-0.336) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.723 (+/-0.417) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.689 (+/-0.374) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.698 (+/-0.383) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.681 (+/-0.372) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.662 (+/-0.373) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.654 (+/-0.379) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [1]
检查交叉验证集中测试集标签是否有预测不到的值: {-1}
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 1.00 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.315) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.587 (+/-0.235) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.590 (+/-0.230) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.666 (+/-0.315) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.611 (+/-0.243) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.613 (+/-0.241) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.590 (+/-0.230) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.315) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.611 (+/-0.243) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.637 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.590 (+/-0.230) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.666 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.661 (+/-0.316) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.637 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.637 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.590 (+/-0.230) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.666 (+/-0.314) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.666 (+/-0.315) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.665 (+/-0.314) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.663 (+/-0.315) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.659 (+/-0.318) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 0.9999999999999997, 'kernel': 'linear'}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6661858974358974
循环迭代之前,delta is: [3.33066907e-16]
SVC模型clf_op_iter的参数是: {'kernel': 'linear', 'decision_function_shape': 'ovr', 'class_weight': {-1: 30}, 'tol': 0.001, 'gamma': 'auto', 'random_state': 0, 'cache_size': 200, 'verbose': False, 'degree': 3, 'C': 0.9999999999999997, 'probability': False, 'shrinking': True, 'coef0': 0.0, 'max_iter': -1}
训练模型SVC对训练样本的预测准确率: 0.9016497461928934
测试集中,预测为舞弊样本的有: (array([ 370, 1248, 1251, 1252, 1255, 1256], dtype=int64),)
测试集中,实际为舞弊样本的有: (array([1246, 1247, 1248, 1249, 1250, 1251, 1252, 1253, 1254, 1255, 1256],
dtype=int64),)
预测的舞弊样本数目: 6
训练模型SVC对测试样本的预测准确率: 0.8851522842639594
以上是第17次特征筛选。
第17次特征筛选,AUC值是: 0.726871443163578
X_train_iter_svc.shape is: (1257, 35)
X_test_iter_svc.shape is: (1257, 35)
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.622 (+/-0.336) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.590 (+/-0.230) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6166666666666667
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.723 (+/-0.417) for {'C': 1.0, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.630 (+/-0.310) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.637 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6661858974358974
RBF的粗grid search最佳超参: {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0} RBF的粗grid search最高分值: 0.6166666666666667
linear的粗grid search最佳超参: {'C': 1.0, 'kernel': 'linear'} linear的粗grid search最高分值: 0.6661858974358974
粗grid search得到的parameter是:
[{'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['rbf'], 'gamma': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.629 (+/-0.329) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.622 (+/-0.336) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.372) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.319) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.622 (+/-0.336) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.674 (+/-0.375) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.310) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.622 (+/-0.336) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.674 (+/-0.375) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.630 (+/-0.310) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.310) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.622 (+/-0.336) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'linear'}
0.689 (+/-0.436) for {'C': 10.0, 'kernel': 'linear'}
0.630 (+/-0.310) for {'C': 100.0, 'kernel': 'linear'}
0.630 (+/-0.310) for {'C': 1000.0, 'kernel': 'linear'}
0.630 (+/-0.310) for {'C': 10000.0, 'kernel': 'linear'}
0.630 (+/-0.310) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [1]
检查交叉验证集中测试集标签是否有预测不到的值: {-1}
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 1.00 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.590 (+/-0.230) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.639 (+/-0.236) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.613 (+/-0.241) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.590 (+/-0.230) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.616 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.639 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.637 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.590 (+/-0.230) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.616 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.639 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.637 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.637 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.590 (+/-0.230) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'linear'}
0.616 (+/-0.238) for {'C': 10.0, 'kernel': 'linear'}
0.637 (+/-0.239) for {'C': 100.0, 'kernel': 'linear'}
0.637 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
0.637 (+/-0.238) for {'C': 10000.0, 'kernel': 'linear'}
0.637 (+/-0.238) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.12 0.17 0.14 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6392628205128205
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.664 (+/-0.388) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.629 (+/-0.329) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.383) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.622 (+/-0.336) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.698 (+/-0.383) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.646 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.319) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.622 (+/-0.336) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.698 (+/-0.383) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.654 (+/-0.393) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.310) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.622 (+/-0.336) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.698 (+/-0.383) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.665 (+/-0.381) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.630 (+/-0.310) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.310) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.622 (+/-0.336) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.723 (+/-0.417) for {'C': 1.0, 'kernel': 'linear'}
0.654 (+/-0.379) for {'C': 10.0, 'kernel': 'linear'}
0.630 (+/-0.310) for {'C': 100.0, 'kernel': 'linear'}
0.630 (+/-0.310) for {'C': 1000.0, 'kernel': 'linear'}
0.630 (+/-0.310) for {'C': 10000.0, 'kernel': 'linear'}
0.630 (+/-0.310) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [1]
检查交叉验证集中测试集标签是否有预测不到的值: {-1}
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 1.00 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.315) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.587 (+/-0.235) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.590 (+/-0.230) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.666 (+/-0.315) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.611 (+/-0.243) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.613 (+/-0.241) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.590 (+/-0.230) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.315) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.611 (+/-0.243) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.637 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.590 (+/-0.230) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.666 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.661 (+/-0.316) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.637 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.637 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.590 (+/-0.230) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.659 (+/-0.318) for {'C': 10.0, 'kernel': 'linear'}
0.637 (+/-0.239) for {'C': 100.0, 'kernel': 'linear'}
0.637 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
0.637 (+/-0.238) for {'C': 10000.0, 'kernel': 'linear'}
0.637 (+/-0.238) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6661858974358974
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.629 (+/-0.329) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.622 (+/-0.336) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.372) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.319) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.622 (+/-0.336) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.674 (+/-0.375) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.310) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.622 (+/-0.336) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.674 (+/-0.375) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.630 (+/-0.310) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.310) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.622 (+/-0.336) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.664 (+/-0.388) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.689 (+/-0.436) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.689 (+/-0.436) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [1]
检查交叉验证集中测试集标签是否有预测不到的值: {-1}
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 1.00 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.590 (+/-0.230) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.639 (+/-0.236) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.613 (+/-0.241) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.590 (+/-0.230) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.616 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.639 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.637 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.590 (+/-0.230) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.616 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.639 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.637 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.637 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.590 (+/-0.230) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.616 (+/-0.238) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.12 0.17 0.14 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6392628205128205
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.664 (+/-0.388) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.629 (+/-0.329) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.383) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.622 (+/-0.336) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.698 (+/-0.383) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.646 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.319) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.622 (+/-0.336) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.698 (+/-0.383) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.654 (+/-0.393) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.310) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.622 (+/-0.336) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.698 (+/-0.383) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.665 (+/-0.381) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.630 (+/-0.310) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.310) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.318) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.622 (+/-0.336) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.723 (+/-0.417) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.689 (+/-0.374) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.698 (+/-0.383) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.681 (+/-0.372) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.662 (+/-0.373) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.654 (+/-0.379) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [1]
检查交叉验证集中测试集标签是否有预测不到的值: {-1}
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 1.00 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.315) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.587 (+/-0.235) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.590 (+/-0.230) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.666 (+/-0.315) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.611 (+/-0.243) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.613 (+/-0.241) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.590 (+/-0.230) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.315) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.611 (+/-0.243) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.637 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.590 (+/-0.230) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.666 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.661 (+/-0.316) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.637 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.637 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.590 (+/-0.230) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.666 (+/-0.314) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.666 (+/-0.315) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.665 (+/-0.314) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.663 (+/-0.315) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.659 (+/-0.318) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 0.9999999999999997, 'kernel': 'linear'}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6661858974358974
循环迭代之前,delta is: [3.33066907e-16]
SVC模型clf_op_iter的参数是: {'kernel': 'linear', 'decision_function_shape': 'ovr', 'class_weight': {-1: 30}, 'tol': 0.001, 'gamma': 'auto', 'random_state': 0, 'cache_size': 200, 'verbose': False, 'degree': 3, 'C': 0.9999999999999997, 'probability': False, 'shrinking': True, 'coef0': 0.0, 'max_iter': -1}
训练模型SVC对训练样本的预测准确率: 0.9016497461928934
测试集中,预测为舞弊样本的有: (array([ 370, 1248, 1252, 1255, 1256], dtype=int64),)
测试集中,实际为舞弊样本的有: (array([1246, 1247, 1248, 1249, 1250, 1251, 1252, 1253, 1254, 1255, 1256],
dtype=int64),)
预测的舞弊样本数目: 5
训练模型SVC对测试样本的预测准确率: 0.8661167512690355
以上是第18次特征筛选。
第18次特征筛选,AUC值是: 0.6814168977090325
X_train_iter_svc.shape is: (1257, 34)
X_test_iter_svc.shape is: (1257, 34)
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.660 (+/-0.389) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.614 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6166666666666667
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.748 (+/-0.449) for {'C': 1.0, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.638 (+/-0.318) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.637 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6663461538461538
RBF的粗grid search最佳超参: {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0} RBF的粗grid search最高分值: 0.6166666666666667
linear的粗grid search最佳超参: {'C': 1.0, 'kernel': 'linear'} linear的粗grid search最高分值: 0.6663461538461538
粗grid search得到的parameter是:
[{'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['rbf'], 'gamma': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.656 (+/-0.392) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.664 (+/-0.388) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.664 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.632 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.664 (+/-0.388) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.662 (+/-0.383) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.632 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.660 (+/-0.389) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.637 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.663 (+/-0.382) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.632 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'linear'}
0.638 (+/-0.318) for {'C': 100.0, 'kernel': 'linear'}
0.638 (+/-0.318) for {'C': 1000.0, 'kernel': 'linear'}
0.638 (+/-0.318) for {'C': 10000.0, 'kernel': 'linear'}
0.638 (+/-0.318) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [1]
检查交叉验证集中测试集标签是否有预测不到的值: {-1}
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 1.00 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.613 (+/-0.241) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.616 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.637 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.616 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.614 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.637 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.637 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'linear'}
0.616 (+/-0.238) for {'C': 10.0, 'kernel': 'linear'}
0.637 (+/-0.237) for {'C': 100.0, 'kernel': 'linear'}
0.637 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
0.637 (+/-0.238) for {'C': 10000.0, 'kernel': 'linear'}
0.637 (+/-0.238) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 100.0, 'kernel': 'linear'}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6375
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.664 (+/-0.388) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.656 (+/-0.392) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.383) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.647 (+/-0.401) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.698 (+/-0.383) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.654 (+/-0.393) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.632 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.698 (+/-0.383) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.653 (+/-0.394) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.662 (+/-0.383) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.632 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.723 (+/-0.423) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.654 (+/-0.393) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.637 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.663 (+/-0.382) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.632 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 1.0, 'kernel': 'linear'}
0.660 (+/-0.384) for {'C': 10.0, 'kernel': 'linear'}
0.638 (+/-0.318) for {'C': 100.0, 'kernel': 'linear'}
0.638 (+/-0.318) for {'C': 1000.0, 'kernel': 'linear'}
0.638 (+/-0.318) for {'C': 10000.0, 'kernel': 'linear'}
0.638 (+/-0.318) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.240) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.315) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.587 (+/-0.236) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.666 (+/-0.315) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.612 (+/-0.242) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.613 (+/-0.241) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.315) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.611 (+/-0.242) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.637 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.666 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.611 (+/-0.243) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.637 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.637 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.660 (+/-0.313) for {'C': 10.0, 'kernel': 'linear'}
0.637 (+/-0.237) for {'C': 100.0, 'kernel': 'linear'}
0.637 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
0.637 (+/-0.238) for {'C': 10000.0, 'kernel': 'linear'}
0.637 (+/-0.238) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6663461538461538
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.656 (+/-0.392) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.664 (+/-0.388) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.664 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.632 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.664 (+/-0.388) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.662 (+/-0.383) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.632 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.660 (+/-0.389) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.637 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.663 (+/-0.382) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.632 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.689 (+/-0.436) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.689 (+/-0.436) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [1]
检查交叉验证集中测试集标签是否有预测不到的值: {-1}
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 1.00 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.613 (+/-0.241) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.616 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.637 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.616 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.614 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.637 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.637 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.616 (+/-0.238) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6373397435897435
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.664 (+/-0.388) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.656 (+/-0.392) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.383) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.647 (+/-0.401) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.698 (+/-0.383) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.654 (+/-0.393) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.632 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.698 (+/-0.383) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.653 (+/-0.394) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.662 (+/-0.383) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.632 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.723 (+/-0.423) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.654 (+/-0.393) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.637 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.663 (+/-0.382) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.632 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.698 (+/-0.383) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.723 (+/-0.423) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.681 (+/-0.372) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.666 (+/-0.371) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.660 (+/-0.384) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.240) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.315) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.587 (+/-0.236) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.666 (+/-0.315) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.612 (+/-0.242) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.613 (+/-0.241) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.315) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.611 (+/-0.242) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.637 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.666 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.611 (+/-0.243) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.637 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.637 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.666 (+/-0.315) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.666 (+/-0.315) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.665 (+/-0.314) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.663 (+/-0.316) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.660 (+/-0.313) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 0.9999999999999997, 'kernel': 'linear'}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6663461538461538
循环迭代之前,delta is: [3.33066907e-16]
SVC模型clf_op_iter的参数是: {'kernel': 'linear', 'decision_function_shape': 'ovr', 'class_weight': {-1: 30}, 'tol': 0.001, 'gamma': 'auto', 'random_state': 0, 'cache_size': 200, 'verbose': False, 'degree': 3, 'C': 0.9999999999999997, 'probability': False, 'shrinking': True, 'coef0': 0.0, 'max_iter': -1}
训练模型SVC对训练样本的预测准确率: 0.9016497461928934
测试集中,预测为舞弊样本的有: (array([ 370, 1248, 1252, 1255, 1256], dtype=int64),)
测试集中,实际为舞弊样本的有: (array([1246, 1247, 1248, 1249, 1250, 1251, 1252, 1253, 1254, 1255, 1256],
dtype=int64),)
预测的舞弊样本数目: 5
训练模型SVC对测试样本的预测准确率: 0.8661167512690355
以上是第19次特征筛选。
第19次特征筛选,AUC值是: 0.6814168977090325
X_train_iter_svc.shape is: (1257, 33)
X_test_iter_svc.shape is: (1257, 33)
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.660 (+/-0.389) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.614 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6166666666666667
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.748 (+/-0.449) for {'C': 1.0, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.638 (+/-0.318) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.637 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6663461538461538
RBF的粗grid search最佳超参: {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0} RBF的粗grid search最高分值: 0.6166666666666667
linear的粗grid search最佳超参: {'C': 1.0, 'kernel': 'linear'} linear的粗grid search最高分值: 0.6663461538461538
粗grid search得到的parameter是:
[{'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['rbf'], 'gamma': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.656 (+/-0.392) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.664 (+/-0.388) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.664 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.632 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.664 (+/-0.388) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.662 (+/-0.383) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.632 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.660 (+/-0.389) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.637 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.663 (+/-0.382) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.632 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'linear'}
0.638 (+/-0.318) for {'C': 100.0, 'kernel': 'linear'}
0.638 (+/-0.318) for {'C': 1000.0, 'kernel': 'linear'}
0.638 (+/-0.318) for {'C': 10000.0, 'kernel': 'linear'}
0.638 (+/-0.318) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [1]
检查交叉验证集中测试集标签是否有预测不到的值: {-1}
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 1.00 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.613 (+/-0.241) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.616 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.637 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.616 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.614 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.637 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.637 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'linear'}
0.616 (+/-0.238) for {'C': 10.0, 'kernel': 'linear'}
0.637 (+/-0.237) for {'C': 100.0, 'kernel': 'linear'}
0.637 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
0.637 (+/-0.238) for {'C': 10000.0, 'kernel': 'linear'}
0.637 (+/-0.238) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 100.0, 'kernel': 'linear'}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6375
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.664 (+/-0.388) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.656 (+/-0.392) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.383) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.647 (+/-0.401) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.698 (+/-0.383) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.654 (+/-0.393) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.632 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.698 (+/-0.383) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.653 (+/-0.394) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.662 (+/-0.383) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.632 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.723 (+/-0.423) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.654 (+/-0.393) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.637 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.663 (+/-0.382) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.632 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 1.0, 'kernel': 'linear'}
0.660 (+/-0.384) for {'C': 10.0, 'kernel': 'linear'}
0.638 (+/-0.318) for {'C': 100.0, 'kernel': 'linear'}
0.638 (+/-0.318) for {'C': 1000.0, 'kernel': 'linear'}
0.638 (+/-0.318) for {'C': 10000.0, 'kernel': 'linear'}
0.638 (+/-0.318) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.240) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.315) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.587 (+/-0.236) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.666 (+/-0.315) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.612 (+/-0.242) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.613 (+/-0.241) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.315) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.611 (+/-0.242) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.637 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.666 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.611 (+/-0.243) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.637 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.637 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.660 (+/-0.313) for {'C': 10.0, 'kernel': 'linear'}
0.637 (+/-0.237) for {'C': 100.0, 'kernel': 'linear'}
0.637 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
0.637 (+/-0.238) for {'C': 10000.0, 'kernel': 'linear'}
0.637 (+/-0.238) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6663461538461538
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.656 (+/-0.392) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.664 (+/-0.388) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.664 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.632 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.664 (+/-0.388) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.662 (+/-0.383) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.632 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.660 (+/-0.389) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.637 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.663 (+/-0.382) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.632 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.689 (+/-0.436) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.689 (+/-0.436) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [1]
检查交叉验证集中测试集标签是否有预测不到的值: {-1}
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 1.00 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.613 (+/-0.241) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.616 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.637 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.616 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.614 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.637 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.637 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.616 (+/-0.238) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6373397435897435
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.664 (+/-0.388) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.656 (+/-0.392) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.383) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.647 (+/-0.401) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.698 (+/-0.383) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.654 (+/-0.393) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.632 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.698 (+/-0.383) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.653 (+/-0.394) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.662 (+/-0.383) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.632 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.723 (+/-0.423) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.654 (+/-0.393) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.637 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.663 (+/-0.382) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.632 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.698 (+/-0.383) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.698 (+/-0.383) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.681 (+/-0.372) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.666 (+/-0.371) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.660 (+/-0.384) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.240) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.315) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.587 (+/-0.236) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.666 (+/-0.315) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.612 (+/-0.242) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.613 (+/-0.241) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.315) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.611 (+/-0.242) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.637 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.666 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.611 (+/-0.243) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.637 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.637 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.666 (+/-0.315) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.666 (+/-0.315) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.665 (+/-0.314) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.663 (+/-0.316) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.660 (+/-0.313) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 0.9999999999999997, 'kernel': 'linear'}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6663461538461538
循环迭代之前,delta is: [3.33066907e-16]
SVC模型clf_op_iter的参数是: {'kernel': 'linear', 'decision_function_shape': 'ovr', 'class_weight': {-1: 30}, 'tol': 0.001, 'gamma': 'auto', 'random_state': 0, 'cache_size': 200, 'verbose': False, 'degree': 3, 'C': 0.9999999999999997, 'probability': False, 'shrinking': True, 'coef0': 0.0, 'max_iter': -1}
训练模型SVC对训练样本的预测准确率: 0.9016497461928934
测试集中,预测为舞弊样本的有: (array([ 370, 1248, 1252, 1255, 1256], dtype=int64),)
测试集中,实际为舞弊样本的有: (array([1246, 1247, 1248, 1249, 1250, 1251, 1252, 1253, 1254, 1255, 1256],
dtype=int64),)
预测的舞弊样本数目: 5
训练模型SVC对测试样本的预测准确率: 0.8661167512690355
以上是第20次特征筛选。
第20次特征筛选,AUC值是: 0.6814168977090325
X_train_iter_svc.shape is: (1257, 32)
X_test_iter_svc.shape is: (1257, 32)
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.660 (+/-0.389) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.614 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6166666666666667
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.748 (+/-0.449) for {'C': 1.0, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.638 (+/-0.318) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.637 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6663461538461538
RBF的粗grid search最佳超参: {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0} RBF的粗grid search最高分值: 0.6166666666666667
linear的粗grid search最佳超参: {'C': 1.0, 'kernel': 'linear'} linear的粗grid search最高分值: 0.6663461538461538
粗grid search得到的parameter是:
[{'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['rbf'], 'gamma': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.656 (+/-0.392) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.664 (+/-0.388) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.664 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.632 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.664 (+/-0.388) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.662 (+/-0.383) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.632 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.660 (+/-0.389) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.637 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.663 (+/-0.382) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.632 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'linear'}
0.638 (+/-0.318) for {'C': 100.0, 'kernel': 'linear'}
0.638 (+/-0.318) for {'C': 1000.0, 'kernel': 'linear'}
0.638 (+/-0.318) for {'C': 10000.0, 'kernel': 'linear'}
0.638 (+/-0.318) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [1]
检查交叉验证集中测试集标签是否有预测不到的值: {-1}
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 1.00 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.613 (+/-0.241) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.616 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.637 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.616 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.614 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.637 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.637 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'linear'}
0.616 (+/-0.238) for {'C': 10.0, 'kernel': 'linear'}
0.637 (+/-0.237) for {'C': 100.0, 'kernel': 'linear'}
0.637 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
0.637 (+/-0.238) for {'C': 10000.0, 'kernel': 'linear'}
0.637 (+/-0.238) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 100.0, 'kernel': 'linear'}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6375
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.664 (+/-0.388) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.656 (+/-0.392) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.383) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.647 (+/-0.401) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.698 (+/-0.383) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.654 (+/-0.393) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.632 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.698 (+/-0.383) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.653 (+/-0.394) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.662 (+/-0.383) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.632 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.723 (+/-0.423) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.653 (+/-0.394) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.637 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.663 (+/-0.382) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.632 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 1.0, 'kernel': 'linear'}
0.660 (+/-0.384) for {'C': 10.0, 'kernel': 'linear'}
0.638 (+/-0.318) for {'C': 100.0, 'kernel': 'linear'}
0.638 (+/-0.318) for {'C': 1000.0, 'kernel': 'linear'}
0.638 (+/-0.318) for {'C': 10000.0, 'kernel': 'linear'}
0.638 (+/-0.318) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.240) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.315) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.587 (+/-0.236) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.666 (+/-0.315) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.612 (+/-0.242) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.613 (+/-0.241) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.315) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.611 (+/-0.242) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.637 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.666 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.611 (+/-0.242) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.637 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.637 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.660 (+/-0.313) for {'C': 10.0, 'kernel': 'linear'}
0.637 (+/-0.237) for {'C': 100.0, 'kernel': 'linear'}
0.637 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
0.637 (+/-0.238) for {'C': 10000.0, 'kernel': 'linear'}
0.637 (+/-0.238) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6663461538461538
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.656 (+/-0.392) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.664 (+/-0.388) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.664 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.632 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.664 (+/-0.388) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.662 (+/-0.383) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.632 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.660 (+/-0.389) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.637 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.663 (+/-0.382) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.632 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.689 (+/-0.436) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.689 (+/-0.436) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [1]
检查交叉验证集中测试集标签是否有预测不到的值: {-1}
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 1.00 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.613 (+/-0.241) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.616 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.637 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.616 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.614 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.637 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.637 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.616 (+/-0.238) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6373397435897435
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.664 (+/-0.388) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.656 (+/-0.392) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.383) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.647 (+/-0.401) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.698 (+/-0.383) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.654 (+/-0.393) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.632 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.698 (+/-0.383) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.653 (+/-0.394) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.662 (+/-0.383) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.632 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.723 (+/-0.423) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.653 (+/-0.394) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.637 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.663 (+/-0.382) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.632 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.698 (+/-0.383) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.698 (+/-0.383) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.706 (+/-0.418) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.666 (+/-0.371) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.660 (+/-0.384) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.240) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.315) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.587 (+/-0.236) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.666 (+/-0.315) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.612 (+/-0.242) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.613 (+/-0.241) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.315) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.611 (+/-0.242) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.637 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.666 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.611 (+/-0.242) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.637 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.637 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.666 (+/-0.315) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.666 (+/-0.315) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.665 (+/-0.314) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.663 (+/-0.316) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.660 (+/-0.313) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 0.9999999999999997, 'kernel': 'linear'}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6663461538461538
循环迭代之前,delta is: [3.33066907e-16]
SVC模型clf_op_iter的参数是: {'kernel': 'linear', 'decision_function_shape': 'ovr', 'class_weight': {-1: 30}, 'tol': 0.001, 'gamma': 'auto', 'random_state': 0, 'cache_size': 200, 'verbose': False, 'degree': 3, 'C': 0.9999999999999997, 'probability': False, 'shrinking': True, 'coef0': 0.0, 'max_iter': -1}
训练模型SVC对训练样本的预测准确率: 0.9016497461928934
测试集中,预测为舞弊样本的有: (array([ 370, 1248, 1252, 1255, 1256], dtype=int64),)
测试集中,实际为舞弊样本的有: (array([1246, 1247, 1248, 1249, 1250, 1251, 1252, 1253, 1254, 1255, 1256],
dtype=int64),)
预测的舞弊样本数目: 5
训练模型SVC对测试样本的预测准确率: 0.8661167512690355
以上是第21次特征筛选。
第21次特征筛选,AUC值是: 0.6814168977090325
X_train_iter_svc.shape is: (1257, 31)
X_test_iter_svc.shape is: (1257, 31)
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.660 (+/-0.389) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.614 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6166666666666667
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.748 (+/-0.449) for {'C': 1.0, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.638 (+/-0.318) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.637 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6663461538461538
RBF的粗grid search最佳超参: {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0} RBF的粗grid search最高分值: 0.6166666666666667
linear的粗grid search最佳超参: {'C': 1.0, 'kernel': 'linear'} linear的粗grid search最高分值: 0.6663461538461538
粗grid search得到的parameter是:
[{'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['rbf'], 'gamma': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.656 (+/-0.392) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.664 (+/-0.388) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.664 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.632 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.664 (+/-0.388) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.662 (+/-0.383) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.632 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.660 (+/-0.389) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.637 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.654 (+/-0.392) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.632 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'linear'}
0.638 (+/-0.318) for {'C': 100.0, 'kernel': 'linear'}
0.638 (+/-0.318) for {'C': 1000.0, 'kernel': 'linear'}
0.638 (+/-0.318) for {'C': 10000.0, 'kernel': 'linear'}
0.638 (+/-0.318) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [1]
检查交叉验证集中测试集标签是否有预测不到的值: {-1}
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 1.00 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.613 (+/-0.241) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.616 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.637 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.616 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.615 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.637 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.612 (+/-0.241) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'linear'}
0.616 (+/-0.238) for {'C': 10.0, 'kernel': 'linear'}
0.637 (+/-0.237) for {'C': 100.0, 'kernel': 'linear'}
0.637 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
0.637 (+/-0.238) for {'C': 10000.0, 'kernel': 'linear'}
0.637 (+/-0.238) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 100.0, 'kernel': 'linear'}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6375
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.664 (+/-0.388) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.656 (+/-0.392) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.383) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.647 (+/-0.401) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.698 (+/-0.383) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.654 (+/-0.393) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.632 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.698 (+/-0.383) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.653 (+/-0.394) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.662 (+/-0.383) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.632 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.698 (+/-0.383) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.653 (+/-0.394) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.637 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.654 (+/-0.392) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.632 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 1.0, 'kernel': 'linear'}
0.661 (+/-0.384) for {'C': 10.0, 'kernel': 'linear'}
0.638 (+/-0.318) for {'C': 100.0, 'kernel': 'linear'}
0.638 (+/-0.318) for {'C': 1000.0, 'kernel': 'linear'}
0.638 (+/-0.318) for {'C': 10000.0, 'kernel': 'linear'}
0.638 (+/-0.318) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.240) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.315) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.587 (+/-0.236) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.666 (+/-0.315) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.611 (+/-0.243) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.613 (+/-0.241) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.315) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.611 (+/-0.242) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.637 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.666 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.611 (+/-0.242) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.637 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.612 (+/-0.241) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.660 (+/-0.314) for {'C': 10.0, 'kernel': 'linear'}
0.637 (+/-0.237) for {'C': 100.0, 'kernel': 'linear'}
0.637 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
0.637 (+/-0.238) for {'C': 10000.0, 'kernel': 'linear'}
0.637 (+/-0.238) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6663461538461538
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.656 (+/-0.392) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.664 (+/-0.388) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.664 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.632 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.664 (+/-0.388) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.662 (+/-0.383) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.632 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.660 (+/-0.389) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.637 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.654 (+/-0.392) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.632 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.689 (+/-0.436) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.689 (+/-0.436) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [1]
检查交叉验证集中测试集标签是否有预测不到的值: {-1}
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 1.00 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.613 (+/-0.241) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.616 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.637 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.616 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.615 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.637 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.612 (+/-0.241) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.616 (+/-0.238) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6371794871794872
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.664 (+/-0.388) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.656 (+/-0.392) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.383) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.647 (+/-0.401) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.698 (+/-0.383) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.654 (+/-0.393) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.632 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.698 (+/-0.383) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.653 (+/-0.394) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.662 (+/-0.383) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.632 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.698 (+/-0.383) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.653 (+/-0.394) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.637 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.654 (+/-0.392) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.632 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.698 (+/-0.383) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.698 (+/-0.383) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.706 (+/-0.418) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.666 (+/-0.371) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.661 (+/-0.384) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.240) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.315) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.587 (+/-0.236) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.666 (+/-0.315) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.611 (+/-0.243) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.613 (+/-0.241) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.315) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.611 (+/-0.242) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.637 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.666 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.611 (+/-0.242) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.637 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.612 (+/-0.241) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.666 (+/-0.315) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.666 (+/-0.315) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.665 (+/-0.314) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.663 (+/-0.316) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.660 (+/-0.314) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 0.9999999999999997, 'kernel': 'linear'}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6663461538461538
循环迭代之前,delta is: [3.33066907e-16]
SVC模型clf_op_iter的参数是: {'kernel': 'linear', 'decision_function_shape': 'ovr', 'class_weight': {-1: 30}, 'tol': 0.001, 'gamma': 'auto', 'random_state': 0, 'cache_size': 200, 'verbose': False, 'degree': 3, 'C': 0.9999999999999997, 'probability': False, 'shrinking': True, 'coef0': 0.0, 'max_iter': -1}
训练模型SVC对训练样本的预测准确率: 0.9016497461928934
测试集中,预测为舞弊样本的有: (array([1248, 1252, 1255, 1256], dtype=int64),)
测试集中,实际为舞弊样本的有: (array([1246, 1247, 1248, 1249, 1250, 1251, 1252, 1253, 1254, 1255, 1256],
dtype=int64),)
预测的舞弊样本数目: 4
训练模型SVC对测试样本的预测准确率: 0.866751269035533
以上是第22次特征筛选。
第22次特征筛选,AUC值是: 0.6818181818181819
X_train_iter_svc.shape is: (1257, 30)
X_test_iter_svc.shape is: (1257, 30)
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.660 (+/-0.389) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.614 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6166666666666667
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.748 (+/-0.449) for {'C': 1.0, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.637 (+/-0.319) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.637 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6663461538461538
RBF的粗grid search最佳超参: {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0} RBF的粗grid search最高分值: 0.6166666666666667
linear的粗grid search最佳超参: {'C': 1.0, 'kernel': 'linear'} linear的粗grid search最高分值: 0.6663461538461538
粗grid search得到的parameter是:
[{'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['rbf'], 'gamma': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.656 (+/-0.392) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.664 (+/-0.388) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.664 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.632 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.664 (+/-0.388) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.662 (+/-0.383) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.632 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.660 (+/-0.389) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.638 (+/-0.318) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.654 (+/-0.392) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.632 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'linear'}
0.638 (+/-0.318) for {'C': 100.0, 'kernel': 'linear'}
0.637 (+/-0.319) for {'C': 1000.0, 'kernel': 'linear'}
0.637 (+/-0.319) for {'C': 10000.0, 'kernel': 'linear'}
0.637 (+/-0.319) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [1]
检查交叉验证集中测试集标签是否有预测不到的值: {-1}
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 1.00 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.613 (+/-0.241) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.616 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.637 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.613 (+/-0.240) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.616 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.614 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.637 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.612 (+/-0.241) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.613 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'linear'}
0.616 (+/-0.238) for {'C': 10.0, 'kernel': 'linear'}
0.637 (+/-0.237) for {'C': 100.0, 'kernel': 'linear'}
0.637 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
0.637 (+/-0.237) for {'C': 10000.0, 'kernel': 'linear'}
0.637 (+/-0.237) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.98 0.99 623
avg / total 0.98 0.97 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 100.0, 'kernel': 'linear'}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6375
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.664 (+/-0.388) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.654 (+/-0.392) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.383) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.647 (+/-0.401) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.698 (+/-0.383) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.654 (+/-0.393) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.632 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.698 (+/-0.383) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.653 (+/-0.394) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.662 (+/-0.383) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.632 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.698 (+/-0.383) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.653 (+/-0.394) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.638 (+/-0.318) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.654 (+/-0.392) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.632 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 1.0, 'kernel': 'linear'}
0.661 (+/-0.384) for {'C': 10.0, 'kernel': 'linear'}
0.638 (+/-0.318) for {'C': 100.0, 'kernel': 'linear'}
0.637 (+/-0.319) for {'C': 1000.0, 'kernel': 'linear'}
0.637 (+/-0.319) for {'C': 10000.0, 'kernel': 'linear'}
0.637 (+/-0.319) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.315) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.587 (+/-0.235) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.666 (+/-0.315) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.612 (+/-0.242) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.613 (+/-0.241) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.315) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.611 (+/-0.243) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.637 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.613 (+/-0.240) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.666 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.611 (+/-0.243) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.637 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.612 (+/-0.241) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.613 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.660 (+/-0.314) for {'C': 10.0, 'kernel': 'linear'}
0.637 (+/-0.237) for {'C': 100.0, 'kernel': 'linear'}
0.637 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
0.637 (+/-0.237) for {'C': 10000.0, 'kernel': 'linear'}
0.637 (+/-0.237) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6663461538461538
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.656 (+/-0.392) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.664 (+/-0.388) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.664 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.632 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.664 (+/-0.388) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.662 (+/-0.383) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.632 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.660 (+/-0.389) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.638 (+/-0.318) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.654 (+/-0.392) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.632 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.689 (+/-0.436) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.689 (+/-0.436) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [1]
检查交叉验证集中测试集标签是否有预测不到的值: {-1}
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 1.00 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.613 (+/-0.241) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.616 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.637 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.613 (+/-0.240) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.616 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.614 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.637 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.612 (+/-0.241) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.613 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.616 (+/-0.238) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6371794871794872
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.664 (+/-0.388) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.654 (+/-0.392) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.383) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.647 (+/-0.401) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.698 (+/-0.383) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.654 (+/-0.393) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.632 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.698 (+/-0.383) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.653 (+/-0.394) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.662 (+/-0.383) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.632 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.698 (+/-0.383) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.653 (+/-0.394) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.638 (+/-0.318) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.654 (+/-0.392) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.632 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.698 (+/-0.383) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.698 (+/-0.383) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.706 (+/-0.418) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.670 (+/-0.370) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.661 (+/-0.384) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.315) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.587 (+/-0.235) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.666 (+/-0.315) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.612 (+/-0.242) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.613 (+/-0.241) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.315) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.611 (+/-0.243) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.637 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.613 (+/-0.240) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.666 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.611 (+/-0.243) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.637 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.612 (+/-0.241) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.613 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.666 (+/-0.315) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.666 (+/-0.315) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.665 (+/-0.314) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.663 (+/-0.315) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.660 (+/-0.314) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 0.9999999999999997, 'kernel': 'linear'}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6663461538461538
循环迭代之前,delta is: [3.33066907e-16]
SVC模型clf_op_iter的参数是: {'kernel': 'linear', 'decision_function_shape': 'ovr', 'class_weight': {-1: 30}, 'tol': 0.001, 'gamma': 'auto', 'random_state': 0, 'cache_size': 200, 'verbose': False, 'degree': 3, 'C': 0.9999999999999997, 'probability': False, 'shrinking': True, 'coef0': 0.0, 'max_iter': -1}
训练模型SVC对训练样本的预测准确率: 0.9016497461928934
测试集中,预测为舞弊样本的有: (array([1248, 1252, 1255, 1256], dtype=int64),)
测试集中,实际为舞弊样本的有: (array([1246, 1247, 1248, 1249, 1250, 1251, 1252, 1253, 1254, 1255, 1256],
dtype=int64),)
预测的舞弊样本数目: 4
训练模型SVC对测试样本的预测准确率: 0.866751269035533
以上是第23次特征筛选。
第23次特征筛选,AUC值是: 0.6818181818181819
X_train_iter_svc.shape is: (1257, 29)
X_test_iter_svc.shape is: (1257, 29)
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.631 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.614 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6166666666666667
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.748 (+/-0.449) for {'C': 1.0, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.643 (+/-0.306) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.637 (+/-0.241) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6663461538461538
RBF的粗grid search最佳超参: {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0} RBF的粗grid search最高分值: 0.6166666666666667
linear的粗grid search最佳超参: {'C': 1.0, 'kernel': 'linear'} linear的粗grid search最高分值: 0.6663461538461538
粗grid search得到的parameter是:
[{'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['rbf'], 'gamma': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.632 (+/-0.327) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.672 (+/-0.392) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.631 (+/-0.327) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.706 (+/-0.424) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.631 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.706 (+/-0.424) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.639 (+/-0.326) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.631 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.706 (+/-0.424) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.639 (+/-0.306) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.631 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'linear'}
0.631 (+/-0.319) for {'C': 100.0, 'kernel': 'linear'}
0.643 (+/-0.306) for {'C': 1000.0, 'kernel': 'linear'}
0.643 (+/-0.306) for {'C': 10000.0, 'kernel': 'linear'}
0.643 (+/-0.306) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [1]
检查交叉验证集中测试集标签是否有预测不到的值: {-1}
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 1.00 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.004) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.240) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.240) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.640 (+/-0.236) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.613 (+/-0.242) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.616 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.640 (+/-0.236) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.613 (+/-0.241) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.616 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.640 (+/-0.236) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.613 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.638 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'linear'}
0.616 (+/-0.238) for {'C': 10.0, 'kernel': 'linear'}
0.613 (+/-0.240) for {'C': 100.0, 'kernel': 'linear'}
0.637 (+/-0.241) for {'C': 1000.0, 'kernel': 'linear'}
0.638 (+/-0.240) for {'C': 10000.0, 'kernel': 'linear'}
0.638 (+/-0.240) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.17 0.17 0.17 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6399038461538461
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.664 (+/-0.388) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.629 (+/-0.328) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.383) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.647 (+/-0.401) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.631 (+/-0.327) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.698 (+/-0.383) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.654 (+/-0.393) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.631 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.698 (+/-0.383) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.653 (+/-0.394) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.639 (+/-0.326) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.631 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.698 (+/-0.383) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.653 (+/-0.394) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.639 (+/-0.306) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.631 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 1.0, 'kernel': 'linear'}
0.660 (+/-0.385) for {'C': 10.0, 'kernel': 'linear'}
0.631 (+/-0.319) for {'C': 100.0, 'kernel': 'linear'}
0.643 (+/-0.306) for {'C': 1000.0, 'kernel': 'linear'}
0.643 (+/-0.306) for {'C': 10000.0, 'kernel': 'linear'}
0.643 (+/-0.306) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.004) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.240) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.315) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.587 (+/-0.236) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.666 (+/-0.315) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.612 (+/-0.242) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.613 (+/-0.242) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.315) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.611 (+/-0.242) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.613 (+/-0.241) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.666 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.611 (+/-0.242) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.613 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.638 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.660 (+/-0.314) for {'C': 10.0, 'kernel': 'linear'}
0.613 (+/-0.240) for {'C': 100.0, 'kernel': 'linear'}
0.637 (+/-0.241) for {'C': 1000.0, 'kernel': 'linear'}
0.638 (+/-0.240) for {'C': 10000.0, 'kernel': 'linear'}
0.638 (+/-0.240) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6663461538461538
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.632 (+/-0.327) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.672 (+/-0.392) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.631 (+/-0.327) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.706 (+/-0.424) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.631 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.706 (+/-0.424) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.639 (+/-0.326) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.631 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.706 (+/-0.424) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.639 (+/-0.306) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.631 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.689 (+/-0.436) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [1]
检查交叉验证集中测试集标签是否有预测不到的值: {-1}
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 1.00 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.004) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.240) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.240) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.640 (+/-0.236) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.613 (+/-0.242) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.616 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.640 (+/-0.236) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.613 (+/-0.241) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.616 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.640 (+/-0.236) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.613 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.638 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.616 (+/-0.238) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.616 (+/-0.238) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.17 0.17 0.17 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6399038461538461
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.664 (+/-0.388) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.629 (+/-0.328) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.383) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.647 (+/-0.401) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.631 (+/-0.327) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.698 (+/-0.383) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.654 (+/-0.393) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.631 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.698 (+/-0.383) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.653 (+/-0.394) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.639 (+/-0.326) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.631 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.698 (+/-0.383) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.653 (+/-0.394) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.639 (+/-0.306) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.631 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.698 (+/-0.383) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.689 (+/-0.374) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.706 (+/-0.418) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.679 (+/-0.373) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.660 (+/-0.385) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.004) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.240) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.315) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.587 (+/-0.236) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.666 (+/-0.315) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.612 (+/-0.242) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.613 (+/-0.242) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.315) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.611 (+/-0.242) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.613 (+/-0.241) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.666 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.611 (+/-0.242) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.613 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.638 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.666 (+/-0.315) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.666 (+/-0.315) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.665 (+/-0.314) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.664 (+/-0.315) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.660 (+/-0.314) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 0.9999999999999997, 'kernel': 'linear'}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6663461538461538
循环迭代之前,delta is: [3.33066907e-16]
SVC模型clf_op_iter的参数是: {'kernel': 'linear', 'decision_function_shape': 'ovr', 'class_weight': {-1: 30}, 'tol': 0.001, 'gamma': 'auto', 'random_state': 0, 'cache_size': 200, 'verbose': False, 'degree': 3, 'C': 0.9999999999999997, 'probability': False, 'shrinking': True, 'coef0': 0.0, 'max_iter': -1}
训练模型SVC对训练样本的预测准确率: 0.9016497461928934
测试集中,预测为舞弊样本的有: (array([1248, 1252, 1255, 1256], dtype=int64),)
测试集中,实际为舞弊样本的有: (array([1246, 1247, 1248, 1249, 1250, 1251, 1252, 1253, 1254, 1255, 1256],
dtype=int64),)
预测的舞弊样本数目: 4
训练模型SVC对测试样本的预测准确率: 0.866751269035533
以上是第24次特征筛选。
第24次特征筛选,AUC值是: 0.6818181818181819
X_train_iter_svc.shape is: (1257, 28)
X_test_iter_svc.shape is: (1257, 28)
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.632 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.614 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6166666666666667
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.748 (+/-0.449) for {'C': 1.0, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.631 (+/-0.319) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.613 (+/-0.240) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6663461538461538
RBF的粗grid search最佳超参: {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0} RBF的粗grid search最高分值: 0.6166666666666667
linear的粗grid search最佳超参: {'C': 1.0, 'kernel': 'linear'} linear的粗grid search最高分值: 0.6663461538461538
粗grid search得到的parameter是:
[{'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['rbf'], 'gamma': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.632 (+/-0.327) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.672 (+/-0.392) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.632 (+/-0.327) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.632 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.639 (+/-0.326) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.632 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.632 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'linear'}
0.643 (+/-0.306) for {'C': 100.0, 'kernel': 'linear'}
0.631 (+/-0.319) for {'C': 1000.0, 'kernel': 'linear'}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'linear'}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [1]
检查交叉验证集中测试集标签是否有预测不到的值: {-1}
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 1.00 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.005) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.240) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.240) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.240) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.588 (+/-0.232) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.616 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.240) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.588 (+/-0.232) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.616 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.615 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.613 (+/-0.241) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.589 (+/-0.231) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'linear'}
0.616 (+/-0.238) for {'C': 10.0, 'kernel': 'linear'}
0.638 (+/-0.239) for {'C': 100.0, 'kernel': 'linear'}
0.613 (+/-0.240) for {'C': 1000.0, 'kernel': 'linear'}
0.613 (+/-0.240) for {'C': 10000.0, 'kernel': 'linear'}
0.613 (+/-0.240) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 100.0, 'kernel': 'linear'}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6381410256410257
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.664 (+/-0.388) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.631 (+/-0.327) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.383) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.647 (+/-0.401) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.632 (+/-0.327) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.698 (+/-0.383) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.653 (+/-0.394) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.632 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.698 (+/-0.383) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.652 (+/-0.395) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.639 (+/-0.326) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.632 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.698 (+/-0.383) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.652 (+/-0.395) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.632 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 1.0, 'kernel': 'linear'}
0.655 (+/-0.392) for {'C': 10.0, 'kernel': 'linear'}
0.643 (+/-0.306) for {'C': 100.0, 'kernel': 'linear'}
0.631 (+/-0.319) for {'C': 1000.0, 'kernel': 'linear'}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'linear'}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.005) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.240) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.315) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.587 (+/-0.236) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.666 (+/-0.315) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.611 (+/-0.242) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.588 (+/-0.232) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.315) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.611 (+/-0.242) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.588 (+/-0.232) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.666 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.611 (+/-0.242) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.613 (+/-0.241) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.589 (+/-0.231) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.635 (+/-0.324) for {'C': 10.0, 'kernel': 'linear'}
0.638 (+/-0.239) for {'C': 100.0, 'kernel': 'linear'}
0.613 (+/-0.240) for {'C': 1000.0, 'kernel': 'linear'}
0.613 (+/-0.240) for {'C': 10000.0, 'kernel': 'linear'}
0.613 (+/-0.240) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6663461538461538
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.632 (+/-0.327) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.672 (+/-0.392) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.632 (+/-0.327) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.632 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.639 (+/-0.326) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.632 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.632 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.689 (+/-0.436) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [1]
检查交叉验证集中测试集标签是否有预测不到的值: {-1}
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 1.00 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.005) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.240) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.240) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.240) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.588 (+/-0.232) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.616 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.240) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.588 (+/-0.232) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.616 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.615 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.613 (+/-0.241) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.589 (+/-0.231) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.616 (+/-0.238) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.616 (+/-0.238) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [1]
检查交叉验证集中测试集标签是否有预测不到的值: {-1}
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 1.00 623
avg / total 0.98 0.99 0.99 629
本轮grid search结果,得到最好的参数选择是: {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6166666666666667
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.664 (+/-0.388) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.631 (+/-0.327) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.383) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.647 (+/-0.401) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.632 (+/-0.327) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.698 (+/-0.383) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.653 (+/-0.394) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.632 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.698 (+/-0.383) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.652 (+/-0.395) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.639 (+/-0.326) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.632 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.698 (+/-0.383) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.652 (+/-0.395) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.632 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.698 (+/-0.383) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.698 (+/-0.383) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.706 (+/-0.418) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.679 (+/-0.373) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.655 (+/-0.392) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.005) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.240) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.315) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.587 (+/-0.236) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.666 (+/-0.315) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.611 (+/-0.242) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.588 (+/-0.232) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.315) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.611 (+/-0.242) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.588 (+/-0.232) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.666 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.611 (+/-0.242) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.613 (+/-0.241) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.589 (+/-0.231) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10000.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100000.0}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.666 (+/-0.315) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.666 (+/-0.315) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.665 (+/-0.313) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.664 (+/-0.314) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.635 (+/-0.324) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 0.9999999999999997, 'kernel': 'linear'}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6663461538461538
循环迭代之前,delta is: [3.33066907e-16]
SVC模型clf_op_iter的参数是: {'kernel': 'linear', 'decision_function_shape': 'ovr', 'class_weight': {-1: 30}, 'tol': 0.001, 'gamma': 'auto', 'random_state': 0, 'cache_size': 200, 'verbose': False, 'degree': 3, 'C': 0.9999999999999997, 'probability': False, 'shrinking': True, 'coef0': 0.0, 'max_iter': -1}
训练模型SVC对训练样本的预测准确率: 0.9022842639593909
测试集中,预测为舞弊样本的有: (array([1248, 1252, 1255, 1256], dtype=int64),)
测试集中,实际为舞弊样本的有: (array([1246, 1247, 1248, 1249, 1250, 1251, 1252, 1253, 1254, 1255, 1256],
dtype=int64),)
预测的舞弊样本数目: 4
训练模型SVC对测试样本的预测准确率: 0.866751269035533
以上是第25次特征筛选。
第25次特征筛选,AUC值是: 0.6818181818181819
X_train_iter_svc.shape is: (1257, 27)
X_test_iter_svc.shape is: (1257, 27)
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.632 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.747 (+/-0.449) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.614 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.641 (+/-0.236) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6413461538461539
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.748 (+/-0.449) for {'C': 1.0, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.626 (+/-0.318) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.612 (+/-0.242) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6663461538461538
RBF的粗grid search最佳超参: {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001} RBF的粗grid search最高分值: 0.6413461538461539
linear的粗grid search最佳超参: {'C': 1.0, 'kernel': 'linear'} linear的粗grid search最高分值: 0.6663461538461538
粗grid search得到的parameter是:
[{'C': array([1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02, 1.e+03, 1.e+04, 1.e+05,
1.e+06, 1.e+07, 1.e+08]), 'kernel': ['rbf'], 'gamma': array([1.e-08, 1.e-07, 1.e-06, 1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01,
1.e+00, 1.e+01, 1.e+02])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.632 (+/-0.327) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.672 (+/-0.392) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.632 (+/-0.327) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.449) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.437) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.632 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.449) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.639 (+/-0.326) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.632 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.747 (+/-0.502) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.697 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.710 (+/-0.420) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.632 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.747 (+/-0.502) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.798 (+/-0.437) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.664 (+/-0.388) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.631 (+/-0.319) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.626 (+/-0.318) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.639 (+/-0.326) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.632 (+/-0.327) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.649 (+/-0.778) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.747 (+/-0.502) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.695 (+/-0.353) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.632 (+/-0.311) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.624 (+/-0.319) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.626 (+/-0.318) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.639 (+/-0.326) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.632 (+/-0.327) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.699 (+/-0.797) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.797 (+/-0.492) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.675 (+/-0.375) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.626 (+/-0.318) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.624 (+/-0.319) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.626 (+/-0.318) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.639 (+/-0.326) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.632 (+/-0.327) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'linear'}
0.722 (+/-0.417) for {'C': 10.0, 'kernel': 'linear'}
0.643 (+/-0.306) for {'C': 100.0, 'kernel': 'linear'}
0.626 (+/-0.318) for {'C': 1000.0, 'kernel': 'linear'}
0.626 (+/-0.318) for {'C': 10000.0, 'kernel': 'linear'}
0.626 (+/-0.318) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.005) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.240) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.240) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.641 (+/-0.236) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.240) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.588 (+/-0.232) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.641 (+/-0.236) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.240) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.588 (+/-0.232) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.613 (+/-0.241) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.617 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.616 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.640 (+/-0.236) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.613 (+/-0.241) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.588 (+/-0.232) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.613 (+/-0.241) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.617 (+/-0.238) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.683 (+/-0.296) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.615 (+/-0.239) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.613 (+/-0.241) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.612 (+/-0.242) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.588 (+/-0.232) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.613 (+/-0.241) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.617 (+/-0.238) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.617 (+/-0.238) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.681 (+/-0.293) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.636 (+/-0.240) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.613 (+/-0.241) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.612 (+/-0.242) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.588 (+/-0.232) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.613 (+/-0.241) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.642 (+/-0.236) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.642 (+/-0.236) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.664 (+/-0.312) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.612 (+/-0.243) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.613 (+/-0.241) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.612 (+/-0.242) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.588 (+/-0.232) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.613 (+/-0.241) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'linear'}
0.641 (+/-0.236) for {'C': 10.0, 'kernel': 'linear'}
0.638 (+/-0.239) for {'C': 100.0, 'kernel': 'linear'}
0.612 (+/-0.242) for {'C': 1000.0, 'kernel': 'linear'}
0.613 (+/-0.241) for {'C': 10000.0, 'kernel': 'linear'}
0.613 (+/-0.241) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6830128205128205
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.689 (+/-0.374) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.629 (+/-0.328) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.383) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.647 (+/-0.401) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.632 (+/-0.327) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.698 (+/-0.383) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.653 (+/-0.394) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.632 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.698 (+/-0.383) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.653 (+/-0.394) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.639 (+/-0.326) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.632 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.747 (+/-0.502) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.797 (+/-0.492) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.698 (+/-0.383) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.653 (+/-0.394) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.632 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.024) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.797 (+/-0.492) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.678 (+/-0.357) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.655 (+/-0.392) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.631 (+/-0.319) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.626 (+/-0.318) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.639 (+/-0.326) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.632 (+/-0.327) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.699 (+/-0.797) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.660 (+/-0.367) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.648 (+/-0.297) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.632 (+/-0.311) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.624 (+/-0.319) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.626 (+/-0.318) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.639 (+/-0.326) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.632 (+/-0.327) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.648 (+/-0.778) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.684 (+/-0.426) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.627 (+/-0.313) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.626 (+/-0.318) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.624 (+/-0.319) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.626 (+/-0.318) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.639 (+/-0.326) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.632 (+/-0.327) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 1.0, 'kernel': 'linear'}
0.655 (+/-0.392) for {'C': 10.0, 'kernel': 'linear'}
0.643 (+/-0.306) for {'C': 100.0, 'kernel': 'linear'}
0.626 (+/-0.318) for {'C': 1000.0, 'kernel': 'linear'}
0.626 (+/-0.318) for {'C': 10000.0, 'kernel': 'linear'}
0.626 (+/-0.318) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.005) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.641 (+/-0.235) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.240) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.315) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.587 (+/-0.236) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.666 (+/-0.315) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.611 (+/-0.242) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.588 (+/-0.232) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.315) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.611 (+/-0.242) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.588 (+/-0.232) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.613 (+/-0.241) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.617 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.642 (+/-0.236) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.666 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.611 (+/-0.243) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.613 (+/-0.241) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.588 (+/-0.232) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.613 (+/-0.241) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.523 (+/-0.138) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.642 (+/-0.236) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.680 (+/-0.291) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.636 (+/-0.323) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.613 (+/-0.241) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.612 (+/-0.242) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.588 (+/-0.232) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.613 (+/-0.241) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.642 (+/-0.236) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.665 (+/-0.312) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.677 (+/-0.294) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.636 (+/-0.240) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.613 (+/-0.241) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.612 (+/-0.242) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.588 (+/-0.232) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.613 (+/-0.241) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.591 (+/-0.324) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.665 (+/-0.314) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.658 (+/-0.314) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.612 (+/-0.243) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.613 (+/-0.241) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.612 (+/-0.242) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.588 (+/-0.232) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.613 (+/-0.241) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.635 (+/-0.325) for {'C': 10.0, 'kernel': 'linear'}
0.638 (+/-0.239) for {'C': 100.0, 'kernel': 'linear'}
0.612 (+/-0.242) for {'C': 1000.0, 'kernel': 'linear'}
0.613 (+/-0.241) for {'C': 10000.0, 'kernel': 'linear'}
0.613 (+/-0.241) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6799669181301015
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.496 (+/-0.001) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.747 (+/-0.502) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.747 (+/-0.502) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.747 (+/-0.502) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.747 (+/-0.502) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.747 (+/-0.502) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.747 (+/-0.502) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.747 (+/-0.502) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.747 (+/-0.502) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.722 (+/-0.473) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.697 (+/-0.437) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.747 (+/-0.502) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.747 (+/-0.502) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.747 (+/-0.502) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.747 (+/-0.502) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.747 (+/-0.502) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.747 (+/-0.502) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.747 (+/-0.502) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.747 (+/-0.502) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.772 (+/-0.473) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.697 (+/-0.437) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.723 (+/-0.423) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.747 (+/-0.502) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.747 (+/-0.502) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.797 (+/-0.492) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.747 (+/-0.502) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.747 (+/-0.502) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.747 (+/-0.502) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.747 (+/-0.502) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.747 (+/-0.449) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.748 (+/-0.449) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.739 (+/-0.452) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.723 (+/-0.423) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.747 (+/-0.502) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.797 (+/-0.492) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.797 (+/-0.492) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.747 (+/-0.502) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.797 (+/-0.492) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.747 (+/-0.502) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.722 (+/-0.473) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.747 (+/-0.449) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.723 (+/-0.417) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.739 (+/-0.452) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.722 (+/-0.417) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.747 (+/-0.502) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.797 (+/-0.492) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.797 (+/-0.492) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.797 (+/-0.492) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.797 (+/-0.492) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.798 (+/-0.492) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.747 (+/-0.449) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.731 (+/-0.422) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.689 (+/-0.436) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.739 (+/-0.452) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.714 (+/-0.418) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.747 (+/-0.502) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.797 (+/-0.492) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.797 (+/-0.492) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.797 (+/-0.492) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.748 (+/-0.449) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.798 (+/-0.437) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.748 (+/-0.449) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.714 (+/-0.418) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.731 (+/-0.422) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.698 (+/-0.383) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.664 (+/-0.388) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.747 (+/-0.502) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.798 (+/-0.492) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.798 (+/-0.492) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.797 (+/-0.492) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.748 (+/-0.449) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.798 (+/-0.437) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.714 (+/-0.418) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.702 (+/-0.421) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.718 (+/-0.416) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.664 (+/-0.317) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.653 (+/-0.394) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.797 (+/-0.492) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.798 (+/-0.492) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.773 (+/-0.474) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.772 (+/-0.473) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.781 (+/-0.417) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.683 (+/-0.353) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.723 (+/-0.417) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.731 (+/-0.422) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.710 (+/-0.420) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.635 (+/-0.308) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.620 (+/-0.321) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.797 (+/-0.492) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.798 (+/-0.492) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.810 (+/-0.465) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.710 (+/-0.420) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.681 (+/-0.354) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.687 (+/-0.351) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.672 (+/-0.392) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.635 (+/-0.326) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.683 (+/-0.378) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.629 (+/-0.311) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.619 (+/-0.321) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.747 (+/-0.502) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.798 (+/-0.492) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.758 (+/-0.484) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.707 (+/-0.423) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.682 (+/-0.355) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.693 (+/-0.350) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.681 (+/-0.401) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.630 (+/-0.320) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.668 (+/-0.379) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.623 (+/-0.319) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.619 (+/-0.321) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.747 (+/-0.502) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.798 (+/-0.492) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.755 (+/-0.488) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.671 (+/-0.378) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.639 (+/-0.308) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.695 (+/-0.353) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.656 (+/-0.347) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.627 (+/-0.318) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.629 (+/-0.311) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.622 (+/-0.319) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.632 (+/-0.311) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'linear'}
0.722 (+/-0.417) for {'C': 10.0, 'kernel': 'linear'}
0.643 (+/-0.306) for {'C': 100.0, 'kernel': 'linear'}
0.626 (+/-0.318) for {'C': 1000.0, 'kernel': 'linear'}
0.626 (+/-0.318) for {'C': 10000.0, 'kernel': 'linear'}
0.626 (+/-0.318) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.500 (+/-0.000) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.617 (+/-0.238) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.617 (+/-0.238) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.617 (+/-0.238) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.617 (+/-0.238) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.617 (+/-0.238) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.617 (+/-0.238) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.617 (+/-0.238) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.617 (+/-0.238) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.617 (+/-0.238) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.616 (+/-0.237) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.617 (+/-0.238) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.617 (+/-0.238) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.617 (+/-0.238) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.617 (+/-0.238) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.617 (+/-0.238) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.617 (+/-0.238) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.617 (+/-0.238) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.617 (+/-0.238) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.642 (+/-0.236) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.616 (+/-0.237) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.666 (+/-0.315) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.617 (+/-0.238) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.617 (+/-0.238) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.642 (+/-0.236) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.617 (+/-0.238) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.617 (+/-0.238) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.617 (+/-0.238) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.617 (+/-0.238) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.641 (+/-0.236) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.666 (+/-0.316) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.666 (+/-0.316) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.666 (+/-0.315) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.617 (+/-0.238) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.642 (+/-0.236) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.642 (+/-0.236) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.617 (+/-0.238) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.642 (+/-0.236) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.617 (+/-0.238) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.617 (+/-0.238) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.641 (+/-0.236) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.666 (+/-0.315) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.666 (+/-0.316) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.641 (+/-0.236) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.617 (+/-0.238) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.642 (+/-0.236) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.642 (+/-0.236) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.642 (+/-0.236) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.642 (+/-0.236) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.667 (+/-0.316) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.641 (+/-0.236) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.666 (+/-0.315) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.616 (+/-0.238) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.666 (+/-0.316) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.641 (+/-0.236) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.617 (+/-0.238) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.642 (+/-0.236) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.642 (+/-0.236) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.642 (+/-0.236) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.666 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.683 (+/-0.296) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.666 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.666 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.666 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.666 (+/-0.315) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.615 (+/-0.239) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.617 (+/-0.238) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.667 (+/-0.316) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.667 (+/-0.316) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.642 (+/-0.236) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.666 (+/-0.316) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.683 (+/-0.296) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.666 (+/-0.316) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.666 (+/-0.316) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.666 (+/-0.314) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.664 (+/-0.316) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.612 (+/-0.242) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.642 (+/-0.236) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.667 (+/-0.316) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.667 (+/-0.316) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.642 (+/-0.236) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.683 (+/-0.296) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.681 (+/-0.292) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.666 (+/-0.316) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.666 (+/-0.316) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.665 (+/-0.314) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.661 (+/-0.317) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.611 (+/-0.242) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.642 (+/-0.236) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.667 (+/-0.316) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.683 (+/-0.296) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.641 (+/-0.235) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.681 (+/-0.294) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.681 (+/-0.293) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.641 (+/-0.326) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.640 (+/-0.324) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.663 (+/-0.314) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.660 (+/-0.315) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.611 (+/-0.242) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.617 (+/-0.238) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.667 (+/-0.316) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.658 (+/-0.311) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.641 (+/-0.235) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.680 (+/-0.291) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.681 (+/-0.294) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.640 (+/-0.326) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.639 (+/-0.324) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.661 (+/-0.315) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.636 (+/-0.325) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.611 (+/-0.242) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.617 (+/-0.238) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.667 (+/-0.316) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.657 (+/-0.310) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.640 (+/-0.234) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.655 (+/-0.305) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.681 (+/-0.293) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.640 (+/-0.327) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.638 (+/-0.325) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.660 (+/-0.314) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.636 (+/-0.324) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.636 (+/-0.240) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'linear'}
0.641 (+/-0.236) for {'C': 10.0, 'kernel': 'linear'}
0.638 (+/-0.239) for {'C': 100.0, 'kernel': 'linear'}
0.612 (+/-0.242) for {'C': 1000.0, 'kernel': 'linear'}
0.613 (+/-0.241) for {'C': 10000.0, 'kernel': 'linear'}
0.613 (+/-0.241) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6830128205128205
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.747 (+/-0.502) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.747 (+/-0.502) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.797 (+/-0.492) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.747 (+/-0.502) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.797 (+/-0.492) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.797 (+/-0.492) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.797 (+/-0.492) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.797 (+/-0.492) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.748 (+/-0.449) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.739 (+/-0.398) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.698 (+/-0.383) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.747 (+/-0.502) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.797 (+/-0.492) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.797 (+/-0.492) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.797 (+/-0.492) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.797 (+/-0.492) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.797 (+/-0.492) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.798 (+/-0.492) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.798 (+/-0.437) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.723 (+/-0.417) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.698 (+/-0.383) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.706 (+/-0.418) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.797 (+/-0.492) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.797 (+/-0.492) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.797 (+/-0.492) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.797 (+/-0.492) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.798 (+/-0.492) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.798 (+/-0.492) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.748 (+/-0.449) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.739 (+/-0.391) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.748 (+/-0.395) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.723 (+/-0.423) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.704 (+/-0.419) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.797 (+/-0.492) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.798 (+/-0.492) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.798 (+/-0.492) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.797 (+/-0.492) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.748 (+/-0.449) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.798 (+/-0.437) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.748 (+/-0.395) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.739 (+/-0.398) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.764 (+/-0.421) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.706 (+/-0.418) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.665 (+/-0.381) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.797 (+/-0.492) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.798 (+/-0.492) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.848 (+/-0.460) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.798 (+/-0.492) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.773 (+/-0.416) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.739 (+/-0.391) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.748 (+/-0.395) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.739 (+/-0.391) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.702 (+/-0.420) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.654 (+/-0.378) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.659 (+/-0.386) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.797 (+/-0.492) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.798 (+/-0.492) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.823 (+/-0.451) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.714 (+/-0.418) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.699 (+/-0.358) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.678 (+/-0.357) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.689 (+/-0.374) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.681 (+/-0.372) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.667 (+/-0.379) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.625 (+/-0.314) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.655 (+/-0.392) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.772 (+/-0.473) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.798 (+/-0.492) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.735 (+/-0.455) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.669 (+/-0.373) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.649 (+/-0.364) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.687 (+/-0.358) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.643 (+/-0.306) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.621 (+/-0.299) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.659 (+/-0.385) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.626 (+/-0.313) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.651 (+/-0.395) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.764 (+/-0.478) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.798 (+/-0.492) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.733 (+/-0.458) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.686 (+/-0.433) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.663 (+/-0.367) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.654 (+/-0.292) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.635 (+/-0.321) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.659 (+/-0.386) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.659 (+/-0.386) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.628 (+/-0.312) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.620 (+/-0.321) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.714 (+/-0.418) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.798 (+/-0.492) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.720 (+/-0.464) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.671 (+/-0.436) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.668 (+/-0.363) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.647 (+/-0.288) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.632 (+/-0.324) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.620 (+/-0.314) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.659 (+/-0.385) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.629 (+/-0.311) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.619 (+/-0.321) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.672 (+/-0.371) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.773 (+/-0.474) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.711 (+/-0.472) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.671 (+/-0.434) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.645 (+/-0.290) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.639 (+/-0.289) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.656 (+/-0.390) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.620 (+/-0.314) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.661 (+/-0.384) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.623 (+/-0.319) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.619 (+/-0.321) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.660 (+/-0.367) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.748 (+/-0.449) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.669 (+/-0.395) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.679 (+/-0.430) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.648 (+/-0.298) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.648 (+/-0.297) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.630 (+/-0.327) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.620 (+/-0.314) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.628 (+/-0.312) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.622 (+/-0.319) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.632 (+/-0.311) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 1.0, 'kernel': 'linear'}
0.655 (+/-0.392) for {'C': 10.0, 'kernel': 'linear'}
0.643 (+/-0.306) for {'C': 100.0, 'kernel': 'linear'}
0.626 (+/-0.318) for {'C': 1000.0, 'kernel': 'linear'}
0.626 (+/-0.318) for {'C': 10000.0, 'kernel': 'linear'}
0.626 (+/-0.318) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.617 (+/-0.238) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.617 (+/-0.238) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.642 (+/-0.236) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.617 (+/-0.238) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.642 (+/-0.236) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.642 (+/-0.236) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.642 (+/-0.236) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.642 (+/-0.236) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.666 (+/-0.316) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.682 (+/-0.295) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.666 (+/-0.315) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.617 (+/-0.238) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.642 (+/-0.236) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.642 (+/-0.236) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.642 (+/-0.236) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.642 (+/-0.236) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.642 (+/-0.236) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.667 (+/-0.316) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.683 (+/-0.296) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.666 (+/-0.315) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.666 (+/-0.315) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.665 (+/-0.313) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.642 (+/-0.236) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.642 (+/-0.236) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.642 (+/-0.236) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.642 (+/-0.236) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.667 (+/-0.316) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.667 (+/-0.316) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.666 (+/-0.316) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.682 (+/-0.294) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.682 (+/-0.295) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.666 (+/-0.315) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.664 (+/-0.314) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.642 (+/-0.236) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.667 (+/-0.316) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.667 (+/-0.316) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.642 (+/-0.236) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.666 (+/-0.316) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.683 (+/-0.296) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.683 (+/-0.295) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.682 (+/-0.295) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.682 (+/-0.295) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.665 (+/-0.315) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.661 (+/-0.314) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.642 (+/-0.236) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.667 (+/-0.316) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.683 (+/-0.296) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.667 (+/-0.316) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.683 (+/-0.296) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.682 (+/-0.295) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.682 (+/-0.295) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.682 (+/-0.296) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.664 (+/-0.313) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.660 (+/-0.314) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.659 (+/-0.312) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.642 (+/-0.236) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.667 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.683 (+/-0.296) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.666 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.681 (+/-0.295) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.680 (+/-0.291) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.665 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.664 (+/-0.315) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.661 (+/-0.314) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.659 (+/-0.313) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.636 (+/-0.323) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.642 (+/-0.236) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.667 (+/-0.316) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.666 (+/-0.316) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.665 (+/-0.313) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.679 (+/-0.290) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.680 (+/-0.292) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.663 (+/-0.317) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.661 (+/-0.316) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.659 (+/-0.314) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.659 (+/-0.313) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.611 (+/-0.241) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.641 (+/-0.236) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.667 (+/-0.316) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.666 (+/-0.315) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.664 (+/-0.314) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.678 (+/-0.288) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.680 (+/-0.292) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.659 (+/-0.317) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.659 (+/-0.315) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.659 (+/-0.313) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.660 (+/-0.315) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.611 (+/-0.242) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.641 (+/-0.235) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.667 (+/-0.316) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.657 (+/-0.310) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.663 (+/-0.314) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.678 (+/-0.288) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.679 (+/-0.290) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.656 (+/-0.316) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.656 (+/-0.317) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.659 (+/-0.313) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.660 (+/-0.315) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.611 (+/-0.242) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.665 (+/-0.313) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.667 (+/-0.316) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.656 (+/-0.310) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.663 (+/-0.315) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.678 (+/-0.288) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.678 (+/-0.290) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.654 (+/-0.316) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.655 (+/-0.317) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.660 (+/-0.313) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.636 (+/-0.325) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.611 (+/-0.242) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.665 (+/-0.312) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.666 (+/-0.316) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.655 (+/-0.309) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.664 (+/-0.315) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.676 (+/-0.288) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.677 (+/-0.294) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.653 (+/-0.315) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.656 (+/-0.317) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.660 (+/-0.314) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.636 (+/-0.324) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.636 (+/-0.240) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.635 (+/-0.325) for {'C': 10.0, 'kernel': 'linear'}
0.638 (+/-0.239) for {'C': 100.0, 'kernel': 'linear'}
0.612 (+/-0.242) for {'C': 1000.0, 'kernel': 'linear'}
0.613 (+/-0.241) for {'C': 10000.0, 'kernel': 'linear'}
0.613 (+/-0.241) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6833333333333332
循环迭代之前,delta is: [3.69042656e+05 7.48811357e-07]
执行tunemodel()函数前,使用的grid search parameter是:
[{'C': array([ 398107.1705535 , 436515.83224017, 478630.09232264,
524807.46024977, 575439.93733716, 630957.34448019,
691830.97091894, 758577.57502918, 831763.77110267,
912010.83935591, 1000000. ]), 'kernel': ['rbf'], 'gamma': array([1.58489319e-07, 1.73780083e-07, 1.90546072e-07, 2.08929613e-07,
2.29086765e-07, 2.51188643e-07, 2.75422870e-07, 3.01995172e-07,
3.31131121e-07, 3.63078055e-07, 3.98107171e-07])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.797 (+/-0.492) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.797 (+/-0.492) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.747 (+/-0.502) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.797 (+/-0.492) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.797 (+/-0.492) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.797 (+/-0.492) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.797 (+/-0.492) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.747 (+/-0.502) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.747 (+/-0.502) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.747 (+/-0.502) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.747 (+/-0.502) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.797 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.797 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.747 (+/-0.502) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.797 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.797 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.797 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.797 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.747 (+/-0.502) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.747 (+/-0.502) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.747 (+/-0.502) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.797 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.797 (+/-0.492) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.797 (+/-0.492) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.747 (+/-0.502) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.797 (+/-0.492) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.797 (+/-0.492) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.797 (+/-0.492) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.797 (+/-0.492) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.747 (+/-0.502) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.747 (+/-0.502) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.747 (+/-0.502) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.797 (+/-0.492) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.797 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.797 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.747 (+/-0.502) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.797 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.797 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.797 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.797 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.747 (+/-0.502) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.747 (+/-0.502) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.747 (+/-0.502) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.797 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.797 (+/-0.492) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.797 (+/-0.492) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.797 (+/-0.492) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.797 (+/-0.492) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.797 (+/-0.492) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.797 (+/-0.492) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.797 (+/-0.492) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.747 (+/-0.502) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.747 (+/-0.502) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.747 (+/-0.502) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.797 (+/-0.492) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.797 (+/-0.492) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.798 (+/-0.492) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.797 (+/-0.492) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.798 (+/-0.492) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.797 (+/-0.492) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.797 (+/-0.492) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.797 (+/-0.492) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.797 (+/-0.492) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.747 (+/-0.502) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.747 (+/-0.502) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.797 (+/-0.492) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.797 (+/-0.492) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.798 (+/-0.492) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.797 (+/-0.492) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.798 (+/-0.492) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.797 (+/-0.492) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.797 (+/-0.492) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.797 (+/-0.492) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.797 (+/-0.492) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.747 (+/-0.502) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.747 (+/-0.502) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.797 (+/-0.492) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.797 (+/-0.492) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.798 (+/-0.492) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.797 (+/-0.492) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.798 (+/-0.492) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.797 (+/-0.492) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.797 (+/-0.492) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.797 (+/-0.492) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.797 (+/-0.492) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.747 (+/-0.502) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.747 (+/-0.502) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.797 (+/-0.492) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.797 (+/-0.492) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.798 (+/-0.492) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.797 (+/-0.492) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.798 (+/-0.492) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.797 (+/-0.492) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.797 (+/-0.492) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.797 (+/-0.492) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.797 (+/-0.492) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.747 (+/-0.502) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.747 (+/-0.502) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.797 (+/-0.492) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.797 (+/-0.492) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.798 (+/-0.492) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.797 (+/-0.492) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.798 (+/-0.492) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.797 (+/-0.492) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.797 (+/-0.492) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.797 (+/-0.492) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.797 (+/-0.492) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.747 (+/-0.502) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.797 (+/-0.492) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.797 (+/-0.492) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.797 (+/-0.492) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.798 (+/-0.492) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.797 (+/-0.492) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.798 (+/-0.492) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.797 (+/-0.492) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.797 (+/-0.492) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.797 (+/-0.492) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.797 (+/-0.492) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.747 (+/-0.502) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.797 (+/-0.492) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.797 (+/-0.492) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'linear'}
0.722 (+/-0.417) for {'C': 10.0, 'kernel': 'linear'}
0.643 (+/-0.306) for {'C': 100.0, 'kernel': 'linear'}
0.626 (+/-0.318) for {'C': 1000.0, 'kernel': 'linear'}
0.626 (+/-0.318) for {'C': 10000.0, 'kernel': 'linear'}
0.626 (+/-0.318) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.642 (+/-0.236) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.642 (+/-0.236) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.617 (+/-0.238) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.642 (+/-0.236) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.642 (+/-0.236) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.642 (+/-0.236) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.642 (+/-0.236) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.617 (+/-0.238) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.617 (+/-0.238) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.617 (+/-0.238) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.617 (+/-0.238) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.642 (+/-0.236) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.642 (+/-0.236) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.617 (+/-0.238) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.642 (+/-0.236) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.642 (+/-0.236) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.642 (+/-0.236) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.642 (+/-0.236) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.617 (+/-0.238) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.617 (+/-0.238) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.617 (+/-0.238) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.642 (+/-0.236) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.642 (+/-0.236) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.642 (+/-0.236) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.617 (+/-0.238) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.642 (+/-0.236) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.642 (+/-0.236) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.642 (+/-0.236) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.642 (+/-0.236) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.617 (+/-0.238) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.617 (+/-0.238) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.617 (+/-0.238) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.642 (+/-0.236) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.642 (+/-0.236) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.642 (+/-0.236) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.617 (+/-0.238) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.642 (+/-0.236) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.642 (+/-0.236) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.642 (+/-0.236) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.642 (+/-0.236) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.617 (+/-0.238) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.617 (+/-0.238) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.617 (+/-0.238) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.642 (+/-0.236) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.642 (+/-0.236) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.642 (+/-0.236) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.642 (+/-0.236) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.642 (+/-0.236) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.642 (+/-0.236) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.642 (+/-0.236) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.642 (+/-0.236) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.617 (+/-0.238) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.617 (+/-0.238) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.617 (+/-0.238) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.642 (+/-0.236) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.642 (+/-0.236) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.667 (+/-0.316) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.642 (+/-0.236) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.667 (+/-0.316) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.642 (+/-0.236) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.642 (+/-0.236) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.642 (+/-0.236) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.642 (+/-0.236) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.617 (+/-0.238) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.617 (+/-0.238) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.642 (+/-0.236) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.642 (+/-0.236) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.667 (+/-0.316) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.642 (+/-0.236) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.667 (+/-0.316) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.642 (+/-0.236) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.642 (+/-0.236) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.642 (+/-0.236) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.642 (+/-0.236) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.617 (+/-0.238) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.617 (+/-0.238) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.642 (+/-0.236) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.642 (+/-0.236) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.667 (+/-0.316) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.642 (+/-0.236) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.667 (+/-0.316) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.642 (+/-0.236) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.642 (+/-0.236) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.642 (+/-0.236) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.642 (+/-0.236) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.617 (+/-0.238) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.617 (+/-0.238) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.642 (+/-0.236) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.642 (+/-0.236) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.667 (+/-0.316) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.642 (+/-0.236) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.667 (+/-0.316) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.642 (+/-0.236) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.642 (+/-0.236) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.642 (+/-0.236) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.642 (+/-0.236) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.617 (+/-0.238) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.617 (+/-0.238) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.642 (+/-0.236) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.642 (+/-0.236) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.667 (+/-0.316) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.642 (+/-0.236) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.667 (+/-0.316) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.642 (+/-0.236) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.642 (+/-0.236) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.642 (+/-0.236) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.642 (+/-0.236) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.617 (+/-0.238) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.642 (+/-0.236) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.642 (+/-0.236) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.642 (+/-0.236) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.667 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.642 (+/-0.236) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.667 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.642 (+/-0.236) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.642 (+/-0.236) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.642 (+/-0.236) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.642 (+/-0.236) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.617 (+/-0.238) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.642 (+/-0.236) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.642 (+/-0.236) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'linear'}
0.641 (+/-0.236) for {'C': 10.0, 'kernel': 'linear'}
0.638 (+/-0.239) for {'C': 100.0, 'kernel': 'linear'}
0.612 (+/-0.242) for {'C': 1000.0, 'kernel': 'linear'}
0.613 (+/-0.241) for {'C': 10000.0, 'kernel': 'linear'}
0.613 (+/-0.241) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6666666666666665
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.798 (+/-0.492) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.798 (+/-0.492) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.798 (+/-0.492) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.798 (+/-0.492) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.797 (+/-0.492) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.798 (+/-0.492) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.797 (+/-0.492) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.797 (+/-0.492) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.797 (+/-0.492) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.797 (+/-0.492) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.797 (+/-0.492) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.798 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.798 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.798 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.798 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.797 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.798 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.797 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.797 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.797 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.797 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.797 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.798 (+/-0.492) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.798 (+/-0.492) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.798 (+/-0.492) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.798 (+/-0.492) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.797 (+/-0.492) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.848 (+/-0.460) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.797 (+/-0.492) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.798 (+/-0.492) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.797 (+/-0.492) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.797 (+/-0.492) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.797 (+/-0.492) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.798 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.798 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.798 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.798 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.797 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.848 (+/-0.460) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.797 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.798 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.797 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.798 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.798 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.798 (+/-0.492) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.798 (+/-0.492) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.798 (+/-0.492) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.798 (+/-0.492) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.797 (+/-0.492) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.848 (+/-0.460) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.797 (+/-0.492) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.798 (+/-0.492) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.797 (+/-0.492) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.798 (+/-0.492) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.798 (+/-0.492) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.798 (+/-0.492) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.798 (+/-0.492) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.798 (+/-0.492) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.798 (+/-0.492) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.797 (+/-0.492) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.848 (+/-0.460) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.797 (+/-0.492) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.798 (+/-0.492) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.797 (+/-0.492) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.798 (+/-0.492) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.798 (+/-0.492) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.798 (+/-0.492) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.798 (+/-0.492) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.798 (+/-0.492) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.798 (+/-0.492) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.773 (+/-0.474) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.848 (+/-0.460) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.797 (+/-0.492) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.798 (+/-0.492) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.798 (+/-0.492) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.798 (+/-0.492) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.798 (+/-0.492) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.798 (+/-0.492) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.798 (+/-0.492) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.798 (+/-0.492) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.798 (+/-0.492) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.773 (+/-0.474) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.823 (+/-0.451) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.797 (+/-0.492) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.798 (+/-0.492) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.798 (+/-0.492) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.798 (+/-0.492) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.798 (+/-0.492) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.798 (+/-0.492) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.798 (+/-0.492) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.798 (+/-0.492) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.848 (+/-0.460) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.773 (+/-0.474) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.823 (+/-0.451) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.848 (+/-0.459) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.798 (+/-0.492) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.798 (+/-0.492) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.798 (+/-0.492) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.748 (+/-0.449) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.798 (+/-0.492) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.798 (+/-0.492) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.798 (+/-0.492) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.798 (+/-0.492) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.773 (+/-0.474) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.823 (+/-0.451) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.848 (+/-0.459) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.798 (+/-0.492) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.798 (+/-0.492) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.798 (+/-0.492) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.748 (+/-0.449) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.798 (+/-0.492) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.848 (+/-0.460) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.798 (+/-0.492) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.798 (+/-0.492) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.773 (+/-0.474) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.823 (+/-0.451) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.848 (+/-0.460) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.798 (+/-0.492) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.798 (+/-0.492) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.798 (+/-0.492) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.714 (+/-0.418) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 1.0, 'kernel': 'linear'}
0.655 (+/-0.392) for {'C': 10.0, 'kernel': 'linear'}
0.643 (+/-0.306) for {'C': 100.0, 'kernel': 'linear'}
0.626 (+/-0.318) for {'C': 1000.0, 'kernel': 'linear'}
0.626 (+/-0.318) for {'C': 10000.0, 'kernel': 'linear'}
0.626 (+/-0.318) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.667 (+/-0.316) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.667 (+/-0.316) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.667 (+/-0.316) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.667 (+/-0.316) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.642 (+/-0.236) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.667 (+/-0.316) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.642 (+/-0.236) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.642 (+/-0.236) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.642 (+/-0.236) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.642 (+/-0.236) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.642 (+/-0.236) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.667 (+/-0.316) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.667 (+/-0.316) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.667 (+/-0.316) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.667 (+/-0.316) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.642 (+/-0.236) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.667 (+/-0.316) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.642 (+/-0.236) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.642 (+/-0.236) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.642 (+/-0.236) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.642 (+/-0.236) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.642 (+/-0.236) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.667 (+/-0.316) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.667 (+/-0.316) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.667 (+/-0.316) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.667 (+/-0.316) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.642 (+/-0.236) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.683 (+/-0.296) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.642 (+/-0.236) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.667 (+/-0.316) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.642 (+/-0.236) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.642 (+/-0.236) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.642 (+/-0.236) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.667 (+/-0.316) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.667 (+/-0.316) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.667 (+/-0.316) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.667 (+/-0.316) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.642 (+/-0.236) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.683 (+/-0.296) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.642 (+/-0.236) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.667 (+/-0.316) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.642 (+/-0.236) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.667 (+/-0.316) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.667 (+/-0.316) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.667 (+/-0.316) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.667 (+/-0.316) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.667 (+/-0.316) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.667 (+/-0.316) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.642 (+/-0.236) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.683 (+/-0.296) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.642 (+/-0.236) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.667 (+/-0.316) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.642 (+/-0.236) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.667 (+/-0.316) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.667 (+/-0.316) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.667 (+/-0.316) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.667 (+/-0.316) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.667 (+/-0.316) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.667 (+/-0.316) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.642 (+/-0.236) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.683 (+/-0.296) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.642 (+/-0.236) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.667 (+/-0.316) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.642 (+/-0.236) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.667 (+/-0.316) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.667 (+/-0.316) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.667 (+/-0.316) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.667 (+/-0.316) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.667 (+/-0.316) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.667 (+/-0.316) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.667 (+/-0.316) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.683 (+/-0.296) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.642 (+/-0.236) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.667 (+/-0.316) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.667 (+/-0.316) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.667 (+/-0.316) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.667 (+/-0.316) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.667 (+/-0.316) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.667 (+/-0.316) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.667 (+/-0.316) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.667 (+/-0.316) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.667 (+/-0.316) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.683 (+/-0.296) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.642 (+/-0.236) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.667 (+/-0.316) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.667 (+/-0.316) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.667 (+/-0.316) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.667 (+/-0.316) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.667 (+/-0.316) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.667 (+/-0.316) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.667 (+/-0.316) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.683 (+/-0.296) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.667 (+/-0.316) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.683 (+/-0.296) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.658 (+/-0.217) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.667 (+/-0.316) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.667 (+/-0.316) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.667 (+/-0.316) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.666 (+/-0.316) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.667 (+/-0.316) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.667 (+/-0.316) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.667 (+/-0.316) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.667 (+/-0.316) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.667 (+/-0.316) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.683 (+/-0.296) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.658 (+/-0.217) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.667 (+/-0.316) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.667 (+/-0.316) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.667 (+/-0.316) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.666 (+/-0.316) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.667 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.683 (+/-0.296) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.667 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.667 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.667 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.683 (+/-0.296) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.683 (+/-0.296) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.667 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.667 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.667 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.666 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.635 (+/-0.325) for {'C': 10.0, 'kernel': 'linear'}
0.638 (+/-0.239) for {'C': 100.0, 'kernel': 'linear'}
0.612 (+/-0.242) for {'C': 1000.0, 'kernel': 'linear'}
0.613 (+/-0.241) for {'C': 10000.0, 'kernel': 'linear'}
0.613 (+/-0.241) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6833333333333332
第0轮loop中,tunemodel函数得到的最优参数是: ([{'C': array([436515.83224017, 444631.26746911, 452897.57990362, 461317.57456038,
469894.10860521, 478630.09232264, 487528.49010339, 496592.32145034,
505824.66200311, 515228.64458176, 524807.46024977]), 'kernel': ['rbf'], 'gamma': array([2.29086765e-07, 2.33345806e-07, 2.37684029e-07, 2.42102905e-07,
2.46603934e-07, 2.51188643e-07, 2.55858589e-07, 2.60615355e-07,
2.65460556e-07, 2.70395836e-07, 2.75422870e-07])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}], {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}, 0.6833333333333332)
这是第0次迭代微调C和gamma。
第0次迭代,得到delta: [1.52327252e+05 6.35274710e-22]
执行tunemodel()函数前,使用的grid search parameter是:
[{'C': array([436515.83224017, 444631.26746911, 452897.57990362, 461317.57456038,
469894.10860521, 478630.09232264, 487528.49010339, 496592.32145034,
505824.66200311, 515228.64458176, 524807.46024977]), 'kernel': ['rbf'], 'gamma': array([2.29086765e-07, 2.33345806e-07, 2.37684029e-07, 2.42102905e-07,
2.46603934e-07, 2.51188643e-07, 2.55858589e-07, 2.60615355e-07,
2.65460556e-07, 2.70395836e-07, 2.75422870e-07])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.797 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.797 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.797 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.797 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.797 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.797 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.797 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.797 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.747 (+/-0.502) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.797 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.797 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.797 (+/-0.492) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.747 (+/-0.502) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.797 (+/-0.492) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.797 (+/-0.492) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.797 (+/-0.492) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.797 (+/-0.492) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.797 (+/-0.492) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.797 (+/-0.492) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.747 (+/-0.502) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.797 (+/-0.492) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.797 (+/-0.492) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.797 (+/-0.492) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.797 (+/-0.492) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.797 (+/-0.492) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.797 (+/-0.492) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.797 (+/-0.492) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.797 (+/-0.492) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.797 (+/-0.492) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.797 (+/-0.492) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.747 (+/-0.502) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.797 (+/-0.492) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.797 (+/-0.492) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.797 (+/-0.492) for {'C': 461317.57456037874, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.797 (+/-0.492) for {'C': 461317.57456037874, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.797 (+/-0.492) for {'C': 461317.57456037874, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.797 (+/-0.492) for {'C': 461317.57456037874, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.797 (+/-0.492) for {'C': 461317.57456037874, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.797 (+/-0.492) for {'C': 461317.57456037874, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.797 (+/-0.492) for {'C': 461317.57456037874, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.797 (+/-0.492) for {'C': 461317.57456037874, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.747 (+/-0.502) for {'C': 461317.57456037874, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.797 (+/-0.492) for {'C': 461317.57456037874, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.797 (+/-0.492) for {'C': 461317.57456037874, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.797 (+/-0.492) for {'C': 469894.10860521457, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.747 (+/-0.502) for {'C': 469894.10860521457, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.797 (+/-0.492) for {'C': 469894.10860521457, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.797 (+/-0.492) for {'C': 469894.10860521457, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.797 (+/-0.492) for {'C': 469894.10860521457, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.797 (+/-0.492) for {'C': 469894.10860521457, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.797 (+/-0.492) for {'C': 469894.10860521457, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.797 (+/-0.492) for {'C': 469894.10860521457, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.747 (+/-0.502) for {'C': 469894.10860521457, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.797 (+/-0.492) for {'C': 469894.10860521457, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.797 (+/-0.492) for {'C': 469894.10860521457, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.797 (+/-0.492) for {'C': 478630.09232263727, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.747 (+/-0.502) for {'C': 478630.09232263727, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.797 (+/-0.492) for {'C': 478630.09232263727, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.797 (+/-0.492) for {'C': 478630.09232263727, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.797 (+/-0.492) for {'C': 478630.09232263727, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.797 (+/-0.492) for {'C': 478630.09232263727, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.797 (+/-0.492) for {'C': 478630.09232263727, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.797 (+/-0.492) for {'C': 478630.09232263727, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.747 (+/-0.502) for {'C': 478630.09232263727, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.797 (+/-0.492) for {'C': 478630.09232263727, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.797 (+/-0.492) for {'C': 478630.09232263727, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.797 (+/-0.492) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.747 (+/-0.502) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.797 (+/-0.492) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.797 (+/-0.492) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.797 (+/-0.492) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.797 (+/-0.492) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.797 (+/-0.492) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.797 (+/-0.492) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.747 (+/-0.502) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.797 (+/-0.492) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.797 (+/-0.492) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.797 (+/-0.492) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.747 (+/-0.502) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.797 (+/-0.492) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.797 (+/-0.492) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.797 (+/-0.492) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.797 (+/-0.492) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.797 (+/-0.492) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.797 (+/-0.492) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.747 (+/-0.502) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.797 (+/-0.492) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.797 (+/-0.492) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.797 (+/-0.492) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.747 (+/-0.502) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.797 (+/-0.492) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.797 (+/-0.492) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.797 (+/-0.492) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.797 (+/-0.492) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.797 (+/-0.492) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.797 (+/-0.492) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.747 (+/-0.502) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.797 (+/-0.492) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.797 (+/-0.492) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.797 (+/-0.492) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.747 (+/-0.502) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.797 (+/-0.492) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.797 (+/-0.492) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.797 (+/-0.492) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.797 (+/-0.492) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.797 (+/-0.492) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.797 (+/-0.492) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.747 (+/-0.502) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.797 (+/-0.492) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.797 (+/-0.492) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.797 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.747 (+/-0.502) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.797 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.797 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.797 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.797 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.797 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.797 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.747 (+/-0.502) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.797 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.797 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'linear'}
0.722 (+/-0.417) for {'C': 10.0, 'kernel': 'linear'}
0.643 (+/-0.306) for {'C': 100.0, 'kernel': 'linear'}
0.626 (+/-0.318) for {'C': 1000.0, 'kernel': 'linear'}
0.626 (+/-0.318) for {'C': 10000.0, 'kernel': 'linear'}
0.626 (+/-0.318) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.642 (+/-0.236) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.642 (+/-0.236) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.642 (+/-0.236) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.642 (+/-0.236) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.642 (+/-0.236) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.642 (+/-0.236) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.642 (+/-0.236) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.642 (+/-0.236) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.617 (+/-0.238) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.642 (+/-0.236) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.642 (+/-0.236) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.642 (+/-0.236) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.617 (+/-0.238) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.642 (+/-0.236) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.642 (+/-0.236) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.642 (+/-0.236) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.642 (+/-0.236) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.642 (+/-0.236) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.642 (+/-0.236) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.617 (+/-0.238) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.642 (+/-0.236) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.642 (+/-0.236) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.642 (+/-0.236) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.642 (+/-0.236) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.642 (+/-0.236) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.642 (+/-0.236) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.642 (+/-0.236) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.642 (+/-0.236) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.642 (+/-0.236) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.642 (+/-0.236) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.617 (+/-0.238) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.642 (+/-0.236) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.642 (+/-0.236) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.642 (+/-0.236) for {'C': 461317.57456037874, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.642 (+/-0.236) for {'C': 461317.57456037874, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.642 (+/-0.236) for {'C': 461317.57456037874, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.642 (+/-0.236) for {'C': 461317.57456037874, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.642 (+/-0.236) for {'C': 461317.57456037874, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.642 (+/-0.236) for {'C': 461317.57456037874, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.642 (+/-0.236) for {'C': 461317.57456037874, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.642 (+/-0.236) for {'C': 461317.57456037874, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.617 (+/-0.238) for {'C': 461317.57456037874, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.642 (+/-0.236) for {'C': 461317.57456037874, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.642 (+/-0.236) for {'C': 461317.57456037874, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.642 (+/-0.236) for {'C': 469894.10860521457, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.617 (+/-0.238) for {'C': 469894.10860521457, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.642 (+/-0.236) for {'C': 469894.10860521457, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.642 (+/-0.236) for {'C': 469894.10860521457, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.642 (+/-0.236) for {'C': 469894.10860521457, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.642 (+/-0.236) for {'C': 469894.10860521457, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.642 (+/-0.236) for {'C': 469894.10860521457, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.642 (+/-0.236) for {'C': 469894.10860521457, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.617 (+/-0.238) for {'C': 469894.10860521457, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.642 (+/-0.236) for {'C': 469894.10860521457, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.642 (+/-0.236) for {'C': 469894.10860521457, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.642 (+/-0.236) for {'C': 478630.09232263727, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.617 (+/-0.238) for {'C': 478630.09232263727, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.642 (+/-0.236) for {'C': 478630.09232263727, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.642 (+/-0.236) for {'C': 478630.09232263727, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.642 (+/-0.236) for {'C': 478630.09232263727, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.642 (+/-0.236) for {'C': 478630.09232263727, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.642 (+/-0.236) for {'C': 478630.09232263727, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.642 (+/-0.236) for {'C': 478630.09232263727, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.617 (+/-0.238) for {'C': 478630.09232263727, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.642 (+/-0.236) for {'C': 478630.09232263727, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.642 (+/-0.236) for {'C': 478630.09232263727, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.642 (+/-0.236) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.617 (+/-0.238) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.642 (+/-0.236) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.642 (+/-0.236) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.642 (+/-0.236) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.642 (+/-0.236) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.642 (+/-0.236) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.642 (+/-0.236) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.617 (+/-0.238) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.642 (+/-0.236) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.642 (+/-0.236) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.642 (+/-0.236) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.617 (+/-0.238) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.642 (+/-0.236) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.642 (+/-0.236) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.642 (+/-0.236) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.642 (+/-0.236) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.642 (+/-0.236) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.642 (+/-0.236) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.617 (+/-0.238) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.642 (+/-0.236) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.642 (+/-0.236) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.642 (+/-0.236) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.617 (+/-0.238) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.642 (+/-0.236) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.642 (+/-0.236) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.642 (+/-0.236) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.642 (+/-0.236) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.642 (+/-0.236) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.642 (+/-0.236) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.617 (+/-0.238) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.642 (+/-0.236) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.642 (+/-0.236) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.642 (+/-0.236) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.617 (+/-0.238) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.642 (+/-0.236) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.642 (+/-0.236) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.642 (+/-0.236) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.642 (+/-0.236) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.642 (+/-0.236) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.642 (+/-0.236) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.617 (+/-0.238) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.642 (+/-0.236) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.642 (+/-0.236) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.642 (+/-0.236) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.617 (+/-0.238) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.642 (+/-0.236) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.642 (+/-0.236) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.642 (+/-0.236) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.642 (+/-0.236) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.642 (+/-0.236) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.642 (+/-0.236) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.617 (+/-0.238) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.642 (+/-0.236) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.642 (+/-0.236) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'linear'}
0.641 (+/-0.236) for {'C': 10.0, 'kernel': 'linear'}
0.638 (+/-0.239) for {'C': 100.0, 'kernel': 'linear'}
0.612 (+/-0.242) for {'C': 1000.0, 'kernel': 'linear'}
0.613 (+/-0.241) for {'C': 10000.0, 'kernel': 'linear'}
0.613 (+/-0.241) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6416666666666666
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.797 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.797 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.798 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.797 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.797 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.798 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.797 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.797 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.797 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.797 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.797 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.797 (+/-0.492) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.797 (+/-0.492) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.798 (+/-0.492) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.798 (+/-0.492) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.797 (+/-0.492) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.848 (+/-0.460) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.797 (+/-0.492) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.797 (+/-0.492) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.797 (+/-0.492) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.797 (+/-0.492) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.797 (+/-0.492) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.797 (+/-0.492) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.797 (+/-0.492) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.798 (+/-0.492) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.798 (+/-0.492) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.797 (+/-0.492) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.848 (+/-0.460) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.797 (+/-0.492) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.797 (+/-0.492) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.797 (+/-0.492) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.797 (+/-0.492) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.797 (+/-0.492) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.797 (+/-0.492) for {'C': 461317.57456037874, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.797 (+/-0.492) for {'C': 461317.57456037874, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.798 (+/-0.492) for {'C': 461317.57456037874, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.798 (+/-0.492) for {'C': 461317.57456037874, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.797 (+/-0.492) for {'C': 461317.57456037874, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.848 (+/-0.460) for {'C': 461317.57456037874, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.798 (+/-0.492) for {'C': 461317.57456037874, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.797 (+/-0.492) for {'C': 461317.57456037874, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.797 (+/-0.492) for {'C': 461317.57456037874, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.797 (+/-0.492) for {'C': 461317.57456037874, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.797 (+/-0.492) for {'C': 461317.57456037874, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.797 (+/-0.492) for {'C': 469894.10860521457, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.797 (+/-0.492) for {'C': 469894.10860521457, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.798 (+/-0.492) for {'C': 469894.10860521457, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.798 (+/-0.492) for {'C': 469894.10860521457, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.797 (+/-0.492) for {'C': 469894.10860521457, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.848 (+/-0.460) for {'C': 469894.10860521457, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.798 (+/-0.492) for {'C': 469894.10860521457, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.797 (+/-0.492) for {'C': 469894.10860521457, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.797 (+/-0.492) for {'C': 469894.10860521457, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.797 (+/-0.492) for {'C': 469894.10860521457, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.797 (+/-0.492) for {'C': 469894.10860521457, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.797 (+/-0.492) for {'C': 478630.09232263727, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.797 (+/-0.492) for {'C': 478630.09232263727, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.798 (+/-0.492) for {'C': 478630.09232263727, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.798 (+/-0.492) for {'C': 478630.09232263727, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.797 (+/-0.492) for {'C': 478630.09232263727, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.848 (+/-0.460) for {'C': 478630.09232263727, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.798 (+/-0.492) for {'C': 478630.09232263727, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.797 (+/-0.492) for {'C': 478630.09232263727, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.797 (+/-0.492) for {'C': 478630.09232263727, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.797 (+/-0.492) for {'C': 478630.09232263727, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.797 (+/-0.492) for {'C': 478630.09232263727, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.797 (+/-0.492) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.797 (+/-0.492) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.798 (+/-0.492) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.798 (+/-0.492) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.797 (+/-0.492) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.848 (+/-0.460) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.798 (+/-0.492) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.797 (+/-0.492) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.797 (+/-0.492) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.797 (+/-0.492) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.797 (+/-0.492) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.797 (+/-0.492) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.797 (+/-0.492) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.798 (+/-0.492) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.798 (+/-0.492) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.797 (+/-0.492) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.848 (+/-0.460) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.798 (+/-0.492) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.797 (+/-0.492) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.797 (+/-0.492) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.797 (+/-0.492) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.797 (+/-0.492) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.797 (+/-0.492) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.797 (+/-0.492) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.798 (+/-0.492) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.798 (+/-0.492) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.797 (+/-0.492) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.848 (+/-0.460) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.798 (+/-0.492) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.798 (+/-0.492) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.797 (+/-0.492) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.797 (+/-0.492) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.797 (+/-0.492) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.797 (+/-0.492) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.797 (+/-0.492) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.798 (+/-0.492) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.798 (+/-0.492) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.797 (+/-0.492) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.848 (+/-0.460) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.798 (+/-0.492) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.798 (+/-0.492) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.797 (+/-0.492) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.797 (+/-0.492) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.797 (+/-0.492) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.797 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.797 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.798 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.798 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.797 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.848 (+/-0.460) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.798 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.798 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.797 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.797 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.797 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 1.0, 'kernel': 'linear'}
0.655 (+/-0.392) for {'C': 10.0, 'kernel': 'linear'}
0.643 (+/-0.306) for {'C': 100.0, 'kernel': 'linear'}
0.626 (+/-0.318) for {'C': 1000.0, 'kernel': 'linear'}
0.626 (+/-0.318) for {'C': 10000.0, 'kernel': 'linear'}
0.626 (+/-0.318) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.642 (+/-0.236) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.642 (+/-0.236) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.667 (+/-0.316) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.642 (+/-0.236) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.642 (+/-0.236) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.667 (+/-0.316) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.642 (+/-0.236) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.642 (+/-0.236) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.642 (+/-0.236) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.642 (+/-0.236) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.642 (+/-0.236) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.642 (+/-0.236) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.642 (+/-0.236) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.667 (+/-0.316) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.667 (+/-0.316) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.642 (+/-0.236) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.683 (+/-0.296) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.642 (+/-0.236) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.642 (+/-0.236) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.642 (+/-0.236) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.642 (+/-0.236) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.642 (+/-0.236) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.642 (+/-0.236) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.642 (+/-0.236) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.667 (+/-0.316) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.667 (+/-0.316) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.642 (+/-0.236) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.683 (+/-0.296) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.642 (+/-0.236) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.642 (+/-0.236) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.642 (+/-0.236) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.642 (+/-0.236) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.642 (+/-0.236) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.642 (+/-0.236) for {'C': 461317.57456037874, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.642 (+/-0.236) for {'C': 461317.57456037874, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.667 (+/-0.316) for {'C': 461317.57456037874, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.667 (+/-0.316) for {'C': 461317.57456037874, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.642 (+/-0.236) for {'C': 461317.57456037874, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.683 (+/-0.296) for {'C': 461317.57456037874, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.667 (+/-0.316) for {'C': 461317.57456037874, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.642 (+/-0.236) for {'C': 461317.57456037874, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.642 (+/-0.236) for {'C': 461317.57456037874, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.642 (+/-0.236) for {'C': 461317.57456037874, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.642 (+/-0.236) for {'C': 461317.57456037874, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.642 (+/-0.236) for {'C': 469894.10860521457, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.642 (+/-0.236) for {'C': 469894.10860521457, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.667 (+/-0.316) for {'C': 469894.10860521457, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.667 (+/-0.316) for {'C': 469894.10860521457, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.642 (+/-0.236) for {'C': 469894.10860521457, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.683 (+/-0.296) for {'C': 469894.10860521457, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.667 (+/-0.316) for {'C': 469894.10860521457, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.642 (+/-0.236) for {'C': 469894.10860521457, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.642 (+/-0.236) for {'C': 469894.10860521457, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.642 (+/-0.236) for {'C': 469894.10860521457, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.642 (+/-0.236) for {'C': 469894.10860521457, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.642 (+/-0.236) for {'C': 478630.09232263727, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.642 (+/-0.236) for {'C': 478630.09232263727, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.667 (+/-0.316) for {'C': 478630.09232263727, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.667 (+/-0.316) for {'C': 478630.09232263727, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.642 (+/-0.236) for {'C': 478630.09232263727, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.683 (+/-0.296) for {'C': 478630.09232263727, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.667 (+/-0.316) for {'C': 478630.09232263727, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.642 (+/-0.236) for {'C': 478630.09232263727, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.642 (+/-0.236) for {'C': 478630.09232263727, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.642 (+/-0.236) for {'C': 478630.09232263727, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.642 (+/-0.236) for {'C': 478630.09232263727, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.642 (+/-0.236) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.642 (+/-0.236) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.667 (+/-0.316) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.667 (+/-0.316) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.642 (+/-0.236) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.683 (+/-0.296) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.667 (+/-0.316) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.642 (+/-0.236) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.642 (+/-0.236) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.642 (+/-0.236) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.642 (+/-0.236) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.642 (+/-0.236) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.642 (+/-0.236) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.667 (+/-0.316) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.667 (+/-0.316) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.642 (+/-0.236) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.683 (+/-0.296) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.667 (+/-0.316) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.642 (+/-0.236) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.642 (+/-0.236) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.642 (+/-0.236) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.642 (+/-0.236) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.642 (+/-0.236) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.642 (+/-0.236) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.667 (+/-0.316) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.667 (+/-0.316) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.642 (+/-0.236) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.683 (+/-0.296) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.667 (+/-0.316) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.667 (+/-0.316) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.642 (+/-0.236) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.642 (+/-0.236) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.642 (+/-0.236) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.642 (+/-0.236) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.642 (+/-0.236) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.667 (+/-0.316) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.667 (+/-0.316) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.642 (+/-0.236) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.683 (+/-0.296) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.667 (+/-0.316) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.667 (+/-0.316) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.642 (+/-0.236) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.642 (+/-0.236) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.642 (+/-0.236) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.642 (+/-0.236) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.642 (+/-0.236) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.667 (+/-0.316) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.667 (+/-0.316) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.642 (+/-0.236) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.683 (+/-0.296) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.667 (+/-0.316) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.667 (+/-0.316) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.642 (+/-0.236) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.642 (+/-0.236) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.642 (+/-0.236) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.635 (+/-0.325) for {'C': 10.0, 'kernel': 'linear'}
0.638 (+/-0.239) for {'C': 100.0, 'kernel': 'linear'}
0.612 (+/-0.242) for {'C': 1000.0, 'kernel': 'linear'}
0.613 (+/-0.241) for {'C': 10000.0, 'kernel': 'linear'}
0.613 (+/-0.241) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6833333333333332
第1轮loop中,tunemodel函数得到的最优参数是: ([{'C': array([436515.83224017, 438126.98202249, 439744.07844737, 441367.14346344,
442996.19910036, 444631.26746911, 446272.37076225, 447919.53125428,
449572.77130191, 451232.11334434, 452897.57990362]), 'kernel': ['rbf'], 'gamma': array([2.46603934e-07, 2.47514132e-07, 2.48427689e-07, 2.49344619e-07,
2.50264933e-07, 2.51188643e-07, 2.52115763e-07, 2.53046305e-07,
2.53980281e-07, 2.54917705e-07, 2.55858589e-07])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}], {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}, 0.6833333333333332)
这是第1次迭代微调C和gamma。
第1次迭代,得到delta: [33998.82485353 0. ]
执行tunemodel()函数前,使用的grid search parameter是:
[{'C': array([436515.83224017, 438126.98202249, 439744.07844737, 441367.14346344,
442996.19910036, 444631.26746911, 446272.37076225, 447919.53125428,
449572.77130191, 451232.11334434, 452897.57990362]), 'kernel': ['rbf'], 'gamma': array([2.46603934e-07, 2.47514132e-07, 2.48427689e-07, 2.49344619e-07,
2.50264933e-07, 2.51188643e-07, 2.52115763e-07, 2.53046305e-07,
2.53980281e-07, 2.54917705e-07, 2.55858589e-07])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.797 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.797 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.475141318074313e-07}
0.797 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.4842768936967995e-07}
0.797 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.49344618809783e-07}
0.797 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.50264932573108e-07}
0.797 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.797 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.797 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.530463049461398e-07}
0.797 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.5398028137728204e-07}
0.797 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.797 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.797 (+/-0.492) for {'C': 438126.9820224933, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.797 (+/-0.492) for {'C': 438126.9820224933, 'kernel': 'rbf', 'gamma': 2.475141318074313e-07}
0.797 (+/-0.492) for {'C': 438126.9820224933, 'kernel': 'rbf', 'gamma': 2.4842768936967995e-07}
0.797 (+/-0.492) for {'C': 438126.9820224933, 'kernel': 'rbf', 'gamma': 2.49344618809783e-07}
0.797 (+/-0.492) for {'C': 438126.9820224933, 'kernel': 'rbf', 'gamma': 2.50264932573108e-07}
0.797 (+/-0.492) for {'C': 438126.9820224933, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.797 (+/-0.492) for {'C': 438126.9820224933, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.797 (+/-0.492) for {'C': 438126.9820224933, 'kernel': 'rbf', 'gamma': 2.530463049461398e-07}
0.797 (+/-0.492) for {'C': 438126.9820224933, 'kernel': 'rbf', 'gamma': 2.5398028137728204e-07}
0.797 (+/-0.492) for {'C': 438126.9820224933, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.797 (+/-0.492) for {'C': 438126.9820224933, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.797 (+/-0.492) for {'C': 439744.0784473682, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.797 (+/-0.492) for {'C': 439744.0784473682, 'kernel': 'rbf', 'gamma': 2.475141318074313e-07}
0.797 (+/-0.492) for {'C': 439744.0784473682, 'kernel': 'rbf', 'gamma': 2.4842768936967995e-07}
0.797 (+/-0.492) for {'C': 439744.0784473682, 'kernel': 'rbf', 'gamma': 2.49344618809783e-07}
0.797 (+/-0.492) for {'C': 439744.0784473682, 'kernel': 'rbf', 'gamma': 2.50264932573108e-07}
0.797 (+/-0.492) for {'C': 439744.0784473682, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.797 (+/-0.492) for {'C': 439744.0784473682, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.797 (+/-0.492) for {'C': 439744.0784473682, 'kernel': 'rbf', 'gamma': 2.530463049461398e-07}
0.797 (+/-0.492) for {'C': 439744.0784473682, 'kernel': 'rbf', 'gamma': 2.5398028137728204e-07}
0.797 (+/-0.492) for {'C': 439744.0784473682, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.797 (+/-0.492) for {'C': 439744.0784473682, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.797 (+/-0.492) for {'C': 441367.1434634396, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.797 (+/-0.492) for {'C': 441367.1434634396, 'kernel': 'rbf', 'gamma': 2.475141318074313e-07}
0.797 (+/-0.492) for {'C': 441367.1434634396, 'kernel': 'rbf', 'gamma': 2.4842768936967995e-07}
0.797 (+/-0.492) for {'C': 441367.1434634396, 'kernel': 'rbf', 'gamma': 2.49344618809783e-07}
0.797 (+/-0.492) for {'C': 441367.1434634396, 'kernel': 'rbf', 'gamma': 2.50264932573108e-07}
0.797 (+/-0.492) for {'C': 441367.1434634396, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.797 (+/-0.492) for {'C': 441367.1434634396, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.797 (+/-0.492) for {'C': 441367.1434634396, 'kernel': 'rbf', 'gamma': 2.530463049461398e-07}
0.797 (+/-0.492) for {'C': 441367.1434634396, 'kernel': 'rbf', 'gamma': 2.5398028137728204e-07}
0.797 (+/-0.492) for {'C': 441367.1434634396, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.797 (+/-0.492) for {'C': 441367.1434634396, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.797 (+/-0.492) for {'C': 442996.199100363, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.797 (+/-0.492) for {'C': 442996.199100363, 'kernel': 'rbf', 'gamma': 2.475141318074313e-07}
0.797 (+/-0.492) for {'C': 442996.199100363, 'kernel': 'rbf', 'gamma': 2.4842768936967995e-07}
0.797 (+/-0.492) for {'C': 442996.199100363, 'kernel': 'rbf', 'gamma': 2.49344618809783e-07}
0.797 (+/-0.492) for {'C': 442996.199100363, 'kernel': 'rbf', 'gamma': 2.50264932573108e-07}
0.797 (+/-0.492) for {'C': 442996.199100363, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.797 (+/-0.492) for {'C': 442996.199100363, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.797 (+/-0.492) for {'C': 442996.199100363, 'kernel': 'rbf', 'gamma': 2.530463049461398e-07}
0.797 (+/-0.492) for {'C': 442996.199100363, 'kernel': 'rbf', 'gamma': 2.5398028137728204e-07}
0.797 (+/-0.492) for {'C': 442996.199100363, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.797 (+/-0.492) for {'C': 442996.199100363, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.797 (+/-0.492) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.797 (+/-0.492) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.475141318074313e-07}
0.797 (+/-0.492) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.4842768936967995e-07}
0.797 (+/-0.492) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.49344618809783e-07}
0.797 (+/-0.492) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.50264932573108e-07}
0.797 (+/-0.492) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.797 (+/-0.492) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.797 (+/-0.492) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.530463049461398e-07}
0.797 (+/-0.492) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.5398028137728204e-07}
0.797 (+/-0.492) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.797 (+/-0.492) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.797 (+/-0.492) for {'C': 446272.3707622524, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.797 (+/-0.492) for {'C': 446272.3707622524, 'kernel': 'rbf', 'gamma': 2.475141318074313e-07}
0.797 (+/-0.492) for {'C': 446272.3707622524, 'kernel': 'rbf', 'gamma': 2.4842768936967995e-07}
0.797 (+/-0.492) for {'C': 446272.3707622524, 'kernel': 'rbf', 'gamma': 2.49344618809783e-07}
0.797 (+/-0.492) for {'C': 446272.3707622524, 'kernel': 'rbf', 'gamma': 2.50264932573108e-07}
0.797 (+/-0.492) for {'C': 446272.3707622524, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.797 (+/-0.492) for {'C': 446272.3707622524, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.797 (+/-0.492) for {'C': 446272.3707622524, 'kernel': 'rbf', 'gamma': 2.530463049461398e-07}
0.797 (+/-0.492) for {'C': 446272.3707622524, 'kernel': 'rbf', 'gamma': 2.5398028137728204e-07}
0.797 (+/-0.492) for {'C': 446272.3707622524, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.797 (+/-0.492) for {'C': 446272.3707622524, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.797 (+/-0.492) for {'C': 447919.53125428315, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.797 (+/-0.492) for {'C': 447919.53125428315, 'kernel': 'rbf', 'gamma': 2.475141318074313e-07}
0.797 (+/-0.492) for {'C': 447919.53125428315, 'kernel': 'rbf', 'gamma': 2.4842768936967995e-07}
0.797 (+/-0.492) for {'C': 447919.53125428315, 'kernel': 'rbf', 'gamma': 2.49344618809783e-07}
0.797 (+/-0.492) for {'C': 447919.53125428315, 'kernel': 'rbf', 'gamma': 2.50264932573108e-07}
0.797 (+/-0.492) for {'C': 447919.53125428315, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.797 (+/-0.492) for {'C': 447919.53125428315, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.797 (+/-0.492) for {'C': 447919.53125428315, 'kernel': 'rbf', 'gamma': 2.530463049461398e-07}
0.797 (+/-0.492) for {'C': 447919.53125428315, 'kernel': 'rbf', 'gamma': 2.5398028137728204e-07}
0.797 (+/-0.492) for {'C': 447919.53125428315, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.797 (+/-0.492) for {'C': 447919.53125428315, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.797 (+/-0.492) for {'C': 449572.77130190533, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.797 (+/-0.492) for {'C': 449572.77130190533, 'kernel': 'rbf', 'gamma': 2.475141318074313e-07}
0.797 (+/-0.492) for {'C': 449572.77130190533, 'kernel': 'rbf', 'gamma': 2.4842768936967995e-07}
0.797 (+/-0.492) for {'C': 449572.77130190533, 'kernel': 'rbf', 'gamma': 2.49344618809783e-07}
0.797 (+/-0.492) for {'C': 449572.77130190533, 'kernel': 'rbf', 'gamma': 2.50264932573108e-07}
0.797 (+/-0.492) for {'C': 449572.77130190533, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.797 (+/-0.492) for {'C': 449572.77130190533, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.797 (+/-0.492) for {'C': 449572.77130190533, 'kernel': 'rbf', 'gamma': 2.530463049461398e-07}
0.797 (+/-0.492) for {'C': 449572.77130190533, 'kernel': 'rbf', 'gamma': 2.5398028137728204e-07}
0.797 (+/-0.492) for {'C': 449572.77130190533, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.797 (+/-0.492) for {'C': 449572.77130190533, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.797 (+/-0.492) for {'C': 451232.1133443365, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.797 (+/-0.492) for {'C': 451232.1133443365, 'kernel': 'rbf', 'gamma': 2.475141318074313e-07}
0.797 (+/-0.492) for {'C': 451232.1133443365, 'kernel': 'rbf', 'gamma': 2.4842768936967995e-07}
0.797 (+/-0.492) for {'C': 451232.1133443365, 'kernel': 'rbf', 'gamma': 2.49344618809783e-07}
0.797 (+/-0.492) for {'C': 451232.1133443365, 'kernel': 'rbf', 'gamma': 2.50264932573108e-07}
0.797 (+/-0.492) for {'C': 451232.1133443365, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.797 (+/-0.492) for {'C': 451232.1133443365, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.797 (+/-0.492) for {'C': 451232.1133443365, 'kernel': 'rbf', 'gamma': 2.530463049461398e-07}
0.797 (+/-0.492) for {'C': 451232.1133443365, 'kernel': 'rbf', 'gamma': 2.5398028137728204e-07}
0.797 (+/-0.492) for {'C': 451232.1133443365, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.797 (+/-0.492) for {'C': 451232.1133443365, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.797 (+/-0.492) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.797 (+/-0.492) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.475141318074313e-07}
0.797 (+/-0.492) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.4842768936967995e-07}
0.797 (+/-0.492) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.49344618809783e-07}
0.797 (+/-0.492) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.50264932573108e-07}
0.797 (+/-0.492) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.797 (+/-0.492) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.797 (+/-0.492) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.530463049461398e-07}
0.797 (+/-0.492) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.5398028137728204e-07}
0.797 (+/-0.492) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.797 (+/-0.492) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'linear'}
0.722 (+/-0.417) for {'C': 10.0, 'kernel': 'linear'}
0.643 (+/-0.306) for {'C': 100.0, 'kernel': 'linear'}
0.626 (+/-0.318) for {'C': 1000.0, 'kernel': 'linear'}
0.626 (+/-0.318) for {'C': 10000.0, 'kernel': 'linear'}
0.626 (+/-0.318) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.642 (+/-0.236) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.642 (+/-0.236) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.475141318074313e-07}
0.642 (+/-0.236) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.4842768936967995e-07}
0.642 (+/-0.236) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.49344618809783e-07}
0.642 (+/-0.236) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.50264932573108e-07}
0.642 (+/-0.236) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.642 (+/-0.236) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.642 (+/-0.236) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.530463049461398e-07}
0.642 (+/-0.236) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.5398028137728204e-07}
0.642 (+/-0.236) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.642 (+/-0.236) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.642 (+/-0.236) for {'C': 438126.9820224933, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.642 (+/-0.236) for {'C': 438126.9820224933, 'kernel': 'rbf', 'gamma': 2.475141318074313e-07}
0.642 (+/-0.236) for {'C': 438126.9820224933, 'kernel': 'rbf', 'gamma': 2.4842768936967995e-07}
0.642 (+/-0.236) for {'C': 438126.9820224933, 'kernel': 'rbf', 'gamma': 2.49344618809783e-07}
0.642 (+/-0.236) for {'C': 438126.9820224933, 'kernel': 'rbf', 'gamma': 2.50264932573108e-07}
0.642 (+/-0.236) for {'C': 438126.9820224933, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.642 (+/-0.236) for {'C': 438126.9820224933, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.642 (+/-0.236) for {'C': 438126.9820224933, 'kernel': 'rbf', 'gamma': 2.530463049461398e-07}
0.642 (+/-0.236) for {'C': 438126.9820224933, 'kernel': 'rbf', 'gamma': 2.5398028137728204e-07}
0.642 (+/-0.236) for {'C': 438126.9820224933, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.642 (+/-0.236) for {'C': 438126.9820224933, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.642 (+/-0.236) for {'C': 439744.0784473682, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.642 (+/-0.236) for {'C': 439744.0784473682, 'kernel': 'rbf', 'gamma': 2.475141318074313e-07}
0.642 (+/-0.236) for {'C': 439744.0784473682, 'kernel': 'rbf', 'gamma': 2.4842768936967995e-07}
0.642 (+/-0.236) for {'C': 439744.0784473682, 'kernel': 'rbf', 'gamma': 2.49344618809783e-07}
0.642 (+/-0.236) for {'C': 439744.0784473682, 'kernel': 'rbf', 'gamma': 2.50264932573108e-07}
0.642 (+/-0.236) for {'C': 439744.0784473682, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.642 (+/-0.236) for {'C': 439744.0784473682, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.642 (+/-0.236) for {'C': 439744.0784473682, 'kernel': 'rbf', 'gamma': 2.530463049461398e-07}
0.642 (+/-0.236) for {'C': 439744.0784473682, 'kernel': 'rbf', 'gamma': 2.5398028137728204e-07}
0.642 (+/-0.236) for {'C': 439744.0784473682, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.642 (+/-0.236) for {'C': 439744.0784473682, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.642 (+/-0.236) for {'C': 441367.1434634396, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.642 (+/-0.236) for {'C': 441367.1434634396, 'kernel': 'rbf', 'gamma': 2.475141318074313e-07}
0.642 (+/-0.236) for {'C': 441367.1434634396, 'kernel': 'rbf', 'gamma': 2.4842768936967995e-07}
0.642 (+/-0.236) for {'C': 441367.1434634396, 'kernel': 'rbf', 'gamma': 2.49344618809783e-07}
0.642 (+/-0.236) for {'C': 441367.1434634396, 'kernel': 'rbf', 'gamma': 2.50264932573108e-07}
0.642 (+/-0.236) for {'C': 441367.1434634396, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.642 (+/-0.236) for {'C': 441367.1434634396, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.642 (+/-0.236) for {'C': 441367.1434634396, 'kernel': 'rbf', 'gamma': 2.530463049461398e-07}
0.642 (+/-0.236) for {'C': 441367.1434634396, 'kernel': 'rbf', 'gamma': 2.5398028137728204e-07}
0.642 (+/-0.236) for {'C': 441367.1434634396, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.642 (+/-0.236) for {'C': 441367.1434634396, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.642 (+/-0.236) for {'C': 442996.199100363, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.642 (+/-0.236) for {'C': 442996.199100363, 'kernel': 'rbf', 'gamma': 2.475141318074313e-07}
0.642 (+/-0.236) for {'C': 442996.199100363, 'kernel': 'rbf', 'gamma': 2.4842768936967995e-07}
0.642 (+/-0.236) for {'C': 442996.199100363, 'kernel': 'rbf', 'gamma': 2.49344618809783e-07}
0.642 (+/-0.236) for {'C': 442996.199100363, 'kernel': 'rbf', 'gamma': 2.50264932573108e-07}
0.642 (+/-0.236) for {'C': 442996.199100363, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.642 (+/-0.236) for {'C': 442996.199100363, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.642 (+/-0.236) for {'C': 442996.199100363, 'kernel': 'rbf', 'gamma': 2.530463049461398e-07}
0.642 (+/-0.236) for {'C': 442996.199100363, 'kernel': 'rbf', 'gamma': 2.5398028137728204e-07}
0.642 (+/-0.236) for {'C': 442996.199100363, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.642 (+/-0.236) for {'C': 442996.199100363, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.642 (+/-0.236) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.642 (+/-0.236) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.475141318074313e-07}
0.642 (+/-0.236) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.4842768936967995e-07}
0.642 (+/-0.236) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.49344618809783e-07}
0.642 (+/-0.236) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.50264932573108e-07}
0.642 (+/-0.236) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.642 (+/-0.236) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.642 (+/-0.236) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.530463049461398e-07}
0.642 (+/-0.236) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.5398028137728204e-07}
0.642 (+/-0.236) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.642 (+/-0.236) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.642 (+/-0.236) for {'C': 446272.3707622524, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.642 (+/-0.236) for {'C': 446272.3707622524, 'kernel': 'rbf', 'gamma': 2.475141318074313e-07}
0.642 (+/-0.236) for {'C': 446272.3707622524, 'kernel': 'rbf', 'gamma': 2.4842768936967995e-07}
0.642 (+/-0.236) for {'C': 446272.3707622524, 'kernel': 'rbf', 'gamma': 2.49344618809783e-07}
0.642 (+/-0.236) for {'C': 446272.3707622524, 'kernel': 'rbf', 'gamma': 2.50264932573108e-07}
0.642 (+/-0.236) for {'C': 446272.3707622524, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.642 (+/-0.236) for {'C': 446272.3707622524, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.642 (+/-0.236) for {'C': 446272.3707622524, 'kernel': 'rbf', 'gamma': 2.530463049461398e-07}
0.642 (+/-0.236) for {'C': 446272.3707622524, 'kernel': 'rbf', 'gamma': 2.5398028137728204e-07}
0.642 (+/-0.236) for {'C': 446272.3707622524, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.642 (+/-0.236) for {'C': 446272.3707622524, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.642 (+/-0.236) for {'C': 447919.53125428315, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.642 (+/-0.236) for {'C': 447919.53125428315, 'kernel': 'rbf', 'gamma': 2.475141318074313e-07}
0.642 (+/-0.236) for {'C': 447919.53125428315, 'kernel': 'rbf', 'gamma': 2.4842768936967995e-07}
0.642 (+/-0.236) for {'C': 447919.53125428315, 'kernel': 'rbf', 'gamma': 2.49344618809783e-07}
0.642 (+/-0.236) for {'C': 447919.53125428315, 'kernel': 'rbf', 'gamma': 2.50264932573108e-07}
0.642 (+/-0.236) for {'C': 447919.53125428315, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.642 (+/-0.236) for {'C': 447919.53125428315, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.642 (+/-0.236) for {'C': 447919.53125428315, 'kernel': 'rbf', 'gamma': 2.530463049461398e-07}
0.642 (+/-0.236) for {'C': 447919.53125428315, 'kernel': 'rbf', 'gamma': 2.5398028137728204e-07}
0.642 (+/-0.236) for {'C': 447919.53125428315, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.642 (+/-0.236) for {'C': 447919.53125428315, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.642 (+/-0.236) for {'C': 449572.77130190533, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.642 (+/-0.236) for {'C': 449572.77130190533, 'kernel': 'rbf', 'gamma': 2.475141318074313e-07}
0.642 (+/-0.236) for {'C': 449572.77130190533, 'kernel': 'rbf', 'gamma': 2.4842768936967995e-07}
0.642 (+/-0.236) for {'C': 449572.77130190533, 'kernel': 'rbf', 'gamma': 2.49344618809783e-07}
0.642 (+/-0.236) for {'C': 449572.77130190533, 'kernel': 'rbf', 'gamma': 2.50264932573108e-07}
0.642 (+/-0.236) for {'C': 449572.77130190533, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.642 (+/-0.236) for {'C': 449572.77130190533, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.642 (+/-0.236) for {'C': 449572.77130190533, 'kernel': 'rbf', 'gamma': 2.530463049461398e-07}
0.642 (+/-0.236) for {'C': 449572.77130190533, 'kernel': 'rbf', 'gamma': 2.5398028137728204e-07}
0.642 (+/-0.236) for {'C': 449572.77130190533, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.642 (+/-0.236) for {'C': 449572.77130190533, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.642 (+/-0.236) for {'C': 451232.1133443365, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.642 (+/-0.236) for {'C': 451232.1133443365, 'kernel': 'rbf', 'gamma': 2.475141318074313e-07}
0.642 (+/-0.236) for {'C': 451232.1133443365, 'kernel': 'rbf', 'gamma': 2.4842768936967995e-07}
0.642 (+/-0.236) for {'C': 451232.1133443365, 'kernel': 'rbf', 'gamma': 2.49344618809783e-07}
0.642 (+/-0.236) for {'C': 451232.1133443365, 'kernel': 'rbf', 'gamma': 2.50264932573108e-07}
0.642 (+/-0.236) for {'C': 451232.1133443365, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.642 (+/-0.236) for {'C': 451232.1133443365, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.642 (+/-0.236) for {'C': 451232.1133443365, 'kernel': 'rbf', 'gamma': 2.530463049461398e-07}
0.642 (+/-0.236) for {'C': 451232.1133443365, 'kernel': 'rbf', 'gamma': 2.5398028137728204e-07}
0.642 (+/-0.236) for {'C': 451232.1133443365, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.642 (+/-0.236) for {'C': 451232.1133443365, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.642 (+/-0.236) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.642 (+/-0.236) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.475141318074313e-07}
0.642 (+/-0.236) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.4842768936967995e-07}
0.642 (+/-0.236) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.49344618809783e-07}
0.642 (+/-0.236) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.50264932573108e-07}
0.642 (+/-0.236) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.642 (+/-0.236) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.642 (+/-0.236) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.530463049461398e-07}
0.642 (+/-0.236) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.5398028137728204e-07}
0.642 (+/-0.236) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.642 (+/-0.236) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'linear'}
0.641 (+/-0.236) for {'C': 10.0, 'kernel': 'linear'}
0.638 (+/-0.239) for {'C': 100.0, 'kernel': 'linear'}
0.612 (+/-0.242) for {'C': 1000.0, 'kernel': 'linear'}
0.613 (+/-0.241) for {'C': 10000.0, 'kernel': 'linear'}
0.613 (+/-0.241) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6416666666666666
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.797 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.797 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.475141318074313e-07}
0.797 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.4842768936967995e-07}
0.848 (+/-0.459) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.49344618809783e-07}
0.848 (+/-0.460) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.50264932573108e-07}
0.798 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.798 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.798 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.530463049461398e-07}
0.798 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.5398028137728204e-07}
0.798 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.797 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.797 (+/-0.492) for {'C': 438126.9820224933, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.797 (+/-0.492) for {'C': 438126.9820224933, 'kernel': 'rbf', 'gamma': 2.475141318074313e-07}
0.797 (+/-0.492) for {'C': 438126.9820224933, 'kernel': 'rbf', 'gamma': 2.4842768936967995e-07}
0.848 (+/-0.459) for {'C': 438126.9820224933, 'kernel': 'rbf', 'gamma': 2.49344618809783e-07}
0.848 (+/-0.460) for {'C': 438126.9820224933, 'kernel': 'rbf', 'gamma': 2.50264932573108e-07}
0.798 (+/-0.492) for {'C': 438126.9820224933, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.798 (+/-0.492) for {'C': 438126.9820224933, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.798 (+/-0.492) for {'C': 438126.9820224933, 'kernel': 'rbf', 'gamma': 2.530463049461398e-07}
0.798 (+/-0.492) for {'C': 438126.9820224933, 'kernel': 'rbf', 'gamma': 2.5398028137728204e-07}
0.798 (+/-0.492) for {'C': 438126.9820224933, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.797 (+/-0.492) for {'C': 438126.9820224933, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.797 (+/-0.492) for {'C': 439744.0784473682, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.797 (+/-0.492) for {'C': 439744.0784473682, 'kernel': 'rbf', 'gamma': 2.475141318074313e-07}
0.797 (+/-0.492) for {'C': 439744.0784473682, 'kernel': 'rbf', 'gamma': 2.4842768936967995e-07}
0.848 (+/-0.459) for {'C': 439744.0784473682, 'kernel': 'rbf', 'gamma': 2.49344618809783e-07}
0.848 (+/-0.460) for {'C': 439744.0784473682, 'kernel': 'rbf', 'gamma': 2.50264932573108e-07}
0.848 (+/-0.460) for {'C': 439744.0784473682, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.798 (+/-0.492) for {'C': 439744.0784473682, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.798 (+/-0.492) for {'C': 439744.0784473682, 'kernel': 'rbf', 'gamma': 2.530463049461398e-07}
0.798 (+/-0.492) for {'C': 439744.0784473682, 'kernel': 'rbf', 'gamma': 2.5398028137728204e-07}
0.798 (+/-0.492) for {'C': 439744.0784473682, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.797 (+/-0.492) for {'C': 439744.0784473682, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.797 (+/-0.492) for {'C': 441367.1434634396, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.797 (+/-0.492) for {'C': 441367.1434634396, 'kernel': 'rbf', 'gamma': 2.475141318074313e-07}
0.797 (+/-0.492) for {'C': 441367.1434634396, 'kernel': 'rbf', 'gamma': 2.4842768936967995e-07}
0.848 (+/-0.459) for {'C': 441367.1434634396, 'kernel': 'rbf', 'gamma': 2.49344618809783e-07}
0.848 (+/-0.460) for {'C': 441367.1434634396, 'kernel': 'rbf', 'gamma': 2.50264932573108e-07}
0.848 (+/-0.460) for {'C': 441367.1434634396, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.798 (+/-0.492) for {'C': 441367.1434634396, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.798 (+/-0.492) for {'C': 441367.1434634396, 'kernel': 'rbf', 'gamma': 2.530463049461398e-07}
0.798 (+/-0.492) for {'C': 441367.1434634396, 'kernel': 'rbf', 'gamma': 2.5398028137728204e-07}
0.798 (+/-0.492) for {'C': 441367.1434634396, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.797 (+/-0.492) for {'C': 441367.1434634396, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.797 (+/-0.492) for {'C': 442996.199100363, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.797 (+/-0.492) for {'C': 442996.199100363, 'kernel': 'rbf', 'gamma': 2.475141318074313e-07}
0.797 (+/-0.492) for {'C': 442996.199100363, 'kernel': 'rbf', 'gamma': 2.4842768936967995e-07}
0.848 (+/-0.459) for {'C': 442996.199100363, 'kernel': 'rbf', 'gamma': 2.49344618809783e-07}
0.848 (+/-0.460) for {'C': 442996.199100363, 'kernel': 'rbf', 'gamma': 2.50264932573108e-07}
0.848 (+/-0.460) for {'C': 442996.199100363, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.798 (+/-0.492) for {'C': 442996.199100363, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.798 (+/-0.492) for {'C': 442996.199100363, 'kernel': 'rbf', 'gamma': 2.530463049461398e-07}
0.798 (+/-0.492) for {'C': 442996.199100363, 'kernel': 'rbf', 'gamma': 2.5398028137728204e-07}
0.798 (+/-0.492) for {'C': 442996.199100363, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.797 (+/-0.492) for {'C': 442996.199100363, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.797 (+/-0.492) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.797 (+/-0.492) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.475141318074313e-07}
0.797 (+/-0.492) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.4842768936967995e-07}
0.848 (+/-0.459) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.49344618809783e-07}
0.848 (+/-0.460) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.50264932573108e-07}
0.848 (+/-0.460) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.798 (+/-0.492) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.798 (+/-0.492) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.530463049461398e-07}
0.798 (+/-0.492) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.5398028137728204e-07}
0.798 (+/-0.492) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.797 (+/-0.492) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.797 (+/-0.492) for {'C': 446272.3707622524, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.797 (+/-0.492) for {'C': 446272.3707622524, 'kernel': 'rbf', 'gamma': 2.475141318074313e-07}
0.797 (+/-0.492) for {'C': 446272.3707622524, 'kernel': 'rbf', 'gamma': 2.4842768936967995e-07}
0.848 (+/-0.459) for {'C': 446272.3707622524, 'kernel': 'rbf', 'gamma': 2.49344618809783e-07}
0.848 (+/-0.460) for {'C': 446272.3707622524, 'kernel': 'rbf', 'gamma': 2.50264932573108e-07}
0.798 (+/-0.492) for {'C': 446272.3707622524, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.798 (+/-0.492) for {'C': 446272.3707622524, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.798 (+/-0.492) for {'C': 446272.3707622524, 'kernel': 'rbf', 'gamma': 2.530463049461398e-07}
0.798 (+/-0.492) for {'C': 446272.3707622524, 'kernel': 'rbf', 'gamma': 2.5398028137728204e-07}
0.798 (+/-0.492) for {'C': 446272.3707622524, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.797 (+/-0.492) for {'C': 446272.3707622524, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.797 (+/-0.492) for {'C': 447919.53125428315, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.797 (+/-0.492) for {'C': 447919.53125428315, 'kernel': 'rbf', 'gamma': 2.475141318074313e-07}
0.797 (+/-0.492) for {'C': 447919.53125428315, 'kernel': 'rbf', 'gamma': 2.4842768936967995e-07}
0.848 (+/-0.459) for {'C': 447919.53125428315, 'kernel': 'rbf', 'gamma': 2.49344618809783e-07}
0.848 (+/-0.460) for {'C': 447919.53125428315, 'kernel': 'rbf', 'gamma': 2.50264932573108e-07}
0.848 (+/-0.460) for {'C': 447919.53125428315, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.798 (+/-0.492) for {'C': 447919.53125428315, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.798 (+/-0.492) for {'C': 447919.53125428315, 'kernel': 'rbf', 'gamma': 2.530463049461398e-07}
0.798 (+/-0.492) for {'C': 447919.53125428315, 'kernel': 'rbf', 'gamma': 2.5398028137728204e-07}
0.798 (+/-0.492) for {'C': 447919.53125428315, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.797 (+/-0.492) for {'C': 447919.53125428315, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.797 (+/-0.492) for {'C': 449572.77130190533, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.797 (+/-0.492) for {'C': 449572.77130190533, 'kernel': 'rbf', 'gamma': 2.475141318074313e-07}
0.797 (+/-0.492) for {'C': 449572.77130190533, 'kernel': 'rbf', 'gamma': 2.4842768936967995e-07}
0.848 (+/-0.459) for {'C': 449572.77130190533, 'kernel': 'rbf', 'gamma': 2.49344618809783e-07}
0.848 (+/-0.460) for {'C': 449572.77130190533, 'kernel': 'rbf', 'gamma': 2.50264932573108e-07}
0.848 (+/-0.460) for {'C': 449572.77130190533, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.798 (+/-0.492) for {'C': 449572.77130190533, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.798 (+/-0.492) for {'C': 449572.77130190533, 'kernel': 'rbf', 'gamma': 2.530463049461398e-07}
0.798 (+/-0.492) for {'C': 449572.77130190533, 'kernel': 'rbf', 'gamma': 2.5398028137728204e-07}
0.798 (+/-0.492) for {'C': 449572.77130190533, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.797 (+/-0.492) for {'C': 449572.77130190533, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.797 (+/-0.492) for {'C': 451232.1133443365, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.797 (+/-0.492) for {'C': 451232.1133443365, 'kernel': 'rbf', 'gamma': 2.475141318074313e-07}
0.797 (+/-0.492) for {'C': 451232.1133443365, 'kernel': 'rbf', 'gamma': 2.4842768936967995e-07}
0.848 (+/-0.459) for {'C': 451232.1133443365, 'kernel': 'rbf', 'gamma': 2.49344618809783e-07}
0.848 (+/-0.460) for {'C': 451232.1133443365, 'kernel': 'rbf', 'gamma': 2.50264932573108e-07}
0.848 (+/-0.460) for {'C': 451232.1133443365, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.798 (+/-0.492) for {'C': 451232.1133443365, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.798 (+/-0.492) for {'C': 451232.1133443365, 'kernel': 'rbf', 'gamma': 2.530463049461398e-07}
0.798 (+/-0.492) for {'C': 451232.1133443365, 'kernel': 'rbf', 'gamma': 2.5398028137728204e-07}
0.798 (+/-0.492) for {'C': 451232.1133443365, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.797 (+/-0.492) for {'C': 451232.1133443365, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.797 (+/-0.492) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.797 (+/-0.492) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.475141318074313e-07}
0.797 (+/-0.492) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.4842768936967995e-07}
0.848 (+/-0.459) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.49344618809783e-07}
0.848 (+/-0.460) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.50264932573108e-07}
0.848 (+/-0.460) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.798 (+/-0.492) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.798 (+/-0.492) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.530463049461398e-07}
0.798 (+/-0.492) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.5398028137728204e-07}
0.798 (+/-0.492) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.797 (+/-0.492) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 1.0, 'kernel': 'linear'}
0.655 (+/-0.392) for {'C': 10.0, 'kernel': 'linear'}
0.643 (+/-0.306) for {'C': 100.0, 'kernel': 'linear'}
0.626 (+/-0.318) for {'C': 1000.0, 'kernel': 'linear'}
0.626 (+/-0.318) for {'C': 10000.0, 'kernel': 'linear'}
0.626 (+/-0.318) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.642 (+/-0.236) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.642 (+/-0.236) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.475141318074313e-07}
0.642 (+/-0.236) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.4842768936967995e-07}
0.658 (+/-0.217) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.49344618809783e-07}
0.683 (+/-0.296) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.50264932573108e-07}
0.667 (+/-0.316) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.667 (+/-0.316) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.667 (+/-0.316) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.530463049461398e-07}
0.667 (+/-0.316) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.5398028137728204e-07}
0.667 (+/-0.316) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.642 (+/-0.236) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.642 (+/-0.236) for {'C': 438126.9820224933, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.642 (+/-0.236) for {'C': 438126.9820224933, 'kernel': 'rbf', 'gamma': 2.475141318074313e-07}
0.642 (+/-0.236) for {'C': 438126.9820224933, 'kernel': 'rbf', 'gamma': 2.4842768936967995e-07}
0.658 (+/-0.217) for {'C': 438126.9820224933, 'kernel': 'rbf', 'gamma': 2.49344618809783e-07}
0.683 (+/-0.296) for {'C': 438126.9820224933, 'kernel': 'rbf', 'gamma': 2.50264932573108e-07}
0.667 (+/-0.316) for {'C': 438126.9820224933, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.667 (+/-0.316) for {'C': 438126.9820224933, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.667 (+/-0.316) for {'C': 438126.9820224933, 'kernel': 'rbf', 'gamma': 2.530463049461398e-07}
0.667 (+/-0.316) for {'C': 438126.9820224933, 'kernel': 'rbf', 'gamma': 2.5398028137728204e-07}
0.667 (+/-0.316) for {'C': 438126.9820224933, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.642 (+/-0.236) for {'C': 438126.9820224933, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.642 (+/-0.236) for {'C': 439744.0784473682, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.642 (+/-0.236) for {'C': 439744.0784473682, 'kernel': 'rbf', 'gamma': 2.475141318074313e-07}
0.642 (+/-0.236) for {'C': 439744.0784473682, 'kernel': 'rbf', 'gamma': 2.4842768936967995e-07}
0.658 (+/-0.217) for {'C': 439744.0784473682, 'kernel': 'rbf', 'gamma': 2.49344618809783e-07}
0.683 (+/-0.296) for {'C': 439744.0784473682, 'kernel': 'rbf', 'gamma': 2.50264932573108e-07}
0.683 (+/-0.296) for {'C': 439744.0784473682, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.667 (+/-0.316) for {'C': 439744.0784473682, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.667 (+/-0.316) for {'C': 439744.0784473682, 'kernel': 'rbf', 'gamma': 2.530463049461398e-07}
0.667 (+/-0.316) for {'C': 439744.0784473682, 'kernel': 'rbf', 'gamma': 2.5398028137728204e-07}
0.667 (+/-0.316) for {'C': 439744.0784473682, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.642 (+/-0.236) for {'C': 439744.0784473682, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.642 (+/-0.236) for {'C': 441367.1434634396, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.642 (+/-0.236) for {'C': 441367.1434634396, 'kernel': 'rbf', 'gamma': 2.475141318074313e-07}
0.642 (+/-0.236) for {'C': 441367.1434634396, 'kernel': 'rbf', 'gamma': 2.4842768936967995e-07}
0.658 (+/-0.217) for {'C': 441367.1434634396, 'kernel': 'rbf', 'gamma': 2.49344618809783e-07}
0.683 (+/-0.296) for {'C': 441367.1434634396, 'kernel': 'rbf', 'gamma': 2.50264932573108e-07}
0.683 (+/-0.296) for {'C': 441367.1434634396, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.667 (+/-0.316) for {'C': 441367.1434634396, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.667 (+/-0.316) for {'C': 441367.1434634396, 'kernel': 'rbf', 'gamma': 2.530463049461398e-07}
0.667 (+/-0.316) for {'C': 441367.1434634396, 'kernel': 'rbf', 'gamma': 2.5398028137728204e-07}
0.667 (+/-0.316) for {'C': 441367.1434634396, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.642 (+/-0.236) for {'C': 441367.1434634396, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.642 (+/-0.236) for {'C': 442996.199100363, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.642 (+/-0.236) for {'C': 442996.199100363, 'kernel': 'rbf', 'gamma': 2.475141318074313e-07}
0.642 (+/-0.236) for {'C': 442996.199100363, 'kernel': 'rbf', 'gamma': 2.4842768936967995e-07}
0.658 (+/-0.217) for {'C': 442996.199100363, 'kernel': 'rbf', 'gamma': 2.49344618809783e-07}
0.683 (+/-0.296) for {'C': 442996.199100363, 'kernel': 'rbf', 'gamma': 2.50264932573108e-07}
0.683 (+/-0.296) for {'C': 442996.199100363, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.667 (+/-0.316) for {'C': 442996.199100363, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.667 (+/-0.316) for {'C': 442996.199100363, 'kernel': 'rbf', 'gamma': 2.530463049461398e-07}
0.667 (+/-0.316) for {'C': 442996.199100363, 'kernel': 'rbf', 'gamma': 2.5398028137728204e-07}
0.667 (+/-0.316) for {'C': 442996.199100363, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.642 (+/-0.236) for {'C': 442996.199100363, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.642 (+/-0.236) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.642 (+/-0.236) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.475141318074313e-07}
0.642 (+/-0.236) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.4842768936967995e-07}
0.658 (+/-0.217) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.49344618809783e-07}
0.683 (+/-0.296) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.50264932573108e-07}
0.683 (+/-0.296) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.667 (+/-0.316) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.667 (+/-0.316) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.530463049461398e-07}
0.667 (+/-0.316) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.5398028137728204e-07}
0.667 (+/-0.316) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.642 (+/-0.236) for {'C': 444631.2674691084, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.642 (+/-0.236) for {'C': 446272.3707622524, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.642 (+/-0.236) for {'C': 446272.3707622524, 'kernel': 'rbf', 'gamma': 2.475141318074313e-07}
0.642 (+/-0.236) for {'C': 446272.3707622524, 'kernel': 'rbf', 'gamma': 2.4842768936967995e-07}
0.658 (+/-0.217) for {'C': 446272.3707622524, 'kernel': 'rbf', 'gamma': 2.49344618809783e-07}
0.683 (+/-0.296) for {'C': 446272.3707622524, 'kernel': 'rbf', 'gamma': 2.50264932573108e-07}
0.667 (+/-0.316) for {'C': 446272.3707622524, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.667 (+/-0.316) for {'C': 446272.3707622524, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.667 (+/-0.316) for {'C': 446272.3707622524, 'kernel': 'rbf', 'gamma': 2.530463049461398e-07}
0.667 (+/-0.316) for {'C': 446272.3707622524, 'kernel': 'rbf', 'gamma': 2.5398028137728204e-07}
0.667 (+/-0.316) for {'C': 446272.3707622524, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.642 (+/-0.236) for {'C': 446272.3707622524, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.642 (+/-0.236) for {'C': 447919.53125428315, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.642 (+/-0.236) for {'C': 447919.53125428315, 'kernel': 'rbf', 'gamma': 2.475141318074313e-07}
0.642 (+/-0.236) for {'C': 447919.53125428315, 'kernel': 'rbf', 'gamma': 2.4842768936967995e-07}
0.658 (+/-0.217) for {'C': 447919.53125428315, 'kernel': 'rbf', 'gamma': 2.49344618809783e-07}
0.683 (+/-0.296) for {'C': 447919.53125428315, 'kernel': 'rbf', 'gamma': 2.50264932573108e-07}
0.683 (+/-0.296) for {'C': 447919.53125428315, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.667 (+/-0.316) for {'C': 447919.53125428315, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.667 (+/-0.316) for {'C': 447919.53125428315, 'kernel': 'rbf', 'gamma': 2.530463049461398e-07}
0.667 (+/-0.316) for {'C': 447919.53125428315, 'kernel': 'rbf', 'gamma': 2.5398028137728204e-07}
0.667 (+/-0.316) for {'C': 447919.53125428315, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.642 (+/-0.236) for {'C': 447919.53125428315, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.642 (+/-0.236) for {'C': 449572.77130190533, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.642 (+/-0.236) for {'C': 449572.77130190533, 'kernel': 'rbf', 'gamma': 2.475141318074313e-07}
0.642 (+/-0.236) for {'C': 449572.77130190533, 'kernel': 'rbf', 'gamma': 2.4842768936967995e-07}
0.658 (+/-0.217) for {'C': 449572.77130190533, 'kernel': 'rbf', 'gamma': 2.49344618809783e-07}
0.683 (+/-0.296) for {'C': 449572.77130190533, 'kernel': 'rbf', 'gamma': 2.50264932573108e-07}
0.683 (+/-0.296) for {'C': 449572.77130190533, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.667 (+/-0.316) for {'C': 449572.77130190533, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.667 (+/-0.316) for {'C': 449572.77130190533, 'kernel': 'rbf', 'gamma': 2.530463049461398e-07}
0.667 (+/-0.316) for {'C': 449572.77130190533, 'kernel': 'rbf', 'gamma': 2.5398028137728204e-07}
0.667 (+/-0.316) for {'C': 449572.77130190533, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.642 (+/-0.236) for {'C': 449572.77130190533, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.642 (+/-0.236) for {'C': 451232.1133443365, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.642 (+/-0.236) for {'C': 451232.1133443365, 'kernel': 'rbf', 'gamma': 2.475141318074313e-07}
0.642 (+/-0.236) for {'C': 451232.1133443365, 'kernel': 'rbf', 'gamma': 2.4842768936967995e-07}
0.658 (+/-0.217) for {'C': 451232.1133443365, 'kernel': 'rbf', 'gamma': 2.49344618809783e-07}
0.683 (+/-0.296) for {'C': 451232.1133443365, 'kernel': 'rbf', 'gamma': 2.50264932573108e-07}
0.683 (+/-0.296) for {'C': 451232.1133443365, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.667 (+/-0.316) for {'C': 451232.1133443365, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.667 (+/-0.316) for {'C': 451232.1133443365, 'kernel': 'rbf', 'gamma': 2.530463049461398e-07}
0.667 (+/-0.316) for {'C': 451232.1133443365, 'kernel': 'rbf', 'gamma': 2.5398028137728204e-07}
0.667 (+/-0.316) for {'C': 451232.1133443365, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.642 (+/-0.236) for {'C': 451232.1133443365, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.642 (+/-0.236) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.642 (+/-0.236) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.475141318074313e-07}
0.642 (+/-0.236) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.4842768936967995e-07}
0.658 (+/-0.217) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.49344618809783e-07}
0.683 (+/-0.296) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.50264932573108e-07}
0.683 (+/-0.296) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.667 (+/-0.316) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.667 (+/-0.316) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.530463049461398e-07}
0.667 (+/-0.316) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.5398028137728204e-07}
0.667 (+/-0.316) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.642 (+/-0.236) for {'C': 452897.57990362024, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.635 (+/-0.325) for {'C': 10.0, 'kernel': 'linear'}
0.638 (+/-0.239) for {'C': 100.0, 'kernel': 'linear'}
0.612 (+/-0.242) for {'C': 1000.0, 'kernel': 'linear'}
0.613 (+/-0.241) for {'C': 10000.0, 'kernel': 'linear'}
0.613 (+/-0.241) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.50264932573108e-07}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6833333333333332
发现最优参数C为原先的最大/最小值,直接重新设置超参。
第2轮loop中,tunemodel函数得到的最优参数是: ([{'C': array([4.36515832e+00, 4.36515832e+01, 4.36515832e+02, 4.36515832e+03,
4.36515832e+04, 4.36515832e+05, 4.36515832e+06, 4.36515832e+07,
4.36515832e+08, 4.36515832e+09, 4.36515832e+10]), 'kernel': ['rbf'], 'gamma': array([2.49344619e-07, 2.49528410e-07, 2.49712337e-07, 2.49896400e-07,
2.50080598e-07, 2.50264933e-07, 2.50449403e-07, 2.50634008e-07,
2.50818751e-07, 2.51003629e-07, 2.51188643e-07])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}], {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.50264932573108e-07}, 0.6833333333333332)
这是第2次迭代微调C和gamma。
第2次迭代,得到delta: [8.11543523e+03 9.23710578e-10]
SVC模型clf_op_iter的参数是: {'kernel': 'rbf', 'decision_function_shape': 'ovr', 'class_weight': {-1: 15}, 'tol': 0.001, 'gamma': 2.50264932573108e-07, 'random_state': 0, 'cache_size': 200, 'verbose': False, 'degree': 3, 'C': 436515.83224016585, 'probability': False, 'shrinking': True, 'coef0': 0.0, 'max_iter': -1}
训练模型SVC对训练样本的预测准确率: 0.9362154500354358
测试集中,预测为舞弊样本的有: (array([ 370, 658, 1246, 1247, 1248, 1255, 1256], dtype=int64),)
测试集中,实际为舞弊样本的有: (array([1246, 1247, 1248, 1249, 1250, 1251, 1252, 1253, 1254, 1255, 1256],
dtype=int64),)
预测的舞弊样本数目: 7
训练模型SVC对测试样本的预测准确率: 0.9347980155917789
以上是第26次特征筛选。
第26次特征筛选,AUC值是: 0.7264701590544287
X_train_iter_svc.shape is: (1257, 26)
X_test_iter_svc.shape is: (1257, 26)
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.635 (+/-0.326) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.748 (+/-0.449) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.20 0.17 0.18 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.614 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.666 (+/-0.316) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.20 0.17 0.18 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.99 0.99 629
本轮grid search结果,得到最好的参数选择是: {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6663461538461538
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.748 (+/-0.449) for {'C': 1.0, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.622 (+/-0.319) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.20 0.17 0.18 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.613 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.20 0.17 0.18 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.99 0.99 629
本轮grid search结果,得到最好的参数选择是: {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6663461538461538
RBF的粗grid search最佳超参: {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001} RBF的粗grid search最高分值: 0.6663461538461538
linear的粗grid search最佳超参: {'C': 1.0, 'kernel': 'linear'} linear的粗grid search最高分值: 0.6663461538461538
粗grid search得到的parameter是:
[{'C': array([1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02, 1.e+03, 1.e+04, 1.e+05,
1.e+06, 1.e+07, 1.e+08]), 'kernel': ['rbf'], 'gamma': array([1.e-08, 1.e-07, 1.e-06, 1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01,
1.e+00, 1.e+01, 1.e+02])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.326) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.449) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.664 (+/-0.388) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.631 (+/-0.327) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.748 (+/-0.449) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.375) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.635 (+/-0.326) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.748 (+/-0.449) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.706 (+/-0.383) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.656 (+/-0.312) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.635 (+/-0.326) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.747 (+/-0.502) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.747 (+/-0.449) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.706 (+/-0.383) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.656 (+/-0.312) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.635 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.747 (+/-0.502) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.798 (+/-0.437) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.685 (+/-0.376) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.656 (+/-0.312) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.614 (+/-0.327) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.635 (+/-0.326) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.649 (+/-0.778) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.797 (+/-0.492) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.676 (+/-0.294) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.631 (+/-0.310) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.637 (+/-0.307) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.614 (+/-0.327) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.635 (+/-0.326) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.699 (+/-0.797) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.797 (+/-0.492) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.648 (+/-0.306) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.657 (+/-0.303) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.624 (+/-0.319) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.614 (+/-0.327) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.635 (+/-0.326) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'linear'}
0.706 (+/-0.383) for {'C': 10.0, 'kernel': 'linear'}
0.641 (+/-0.312) for {'C': 100.0, 'kernel': 'linear'}
0.622 (+/-0.319) for {'C': 1000.0, 'kernel': 'linear'}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'linear'}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.004) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.641 (+/-0.236) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.666 (+/-0.316) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.641 (+/-0.236) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.588 (+/-0.232) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.316) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.666 (+/-0.316) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.639 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.617 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.641 (+/-0.236) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.316) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.639 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.588 (+/-0.233) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.617 (+/-0.238) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.683 (+/-0.296) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.665 (+/-0.315) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.664 (+/-0.318) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.588 (+/-0.232) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.587 (+/-0.234) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.238) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.617 (+/-0.238) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.642 (+/-0.236) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.681 (+/-0.295) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.661 (+/-0.315) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.638 (+/-0.238) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.587 (+/-0.233) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.587 (+/-0.234) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.238) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.642 (+/-0.236) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.642 (+/-0.236) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.664 (+/-0.315) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.687 (+/-0.298) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.612 (+/-0.242) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.587 (+/-0.233) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.587 (+/-0.234) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.238) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'linear'}
0.666 (+/-0.315) for {'C': 10.0, 'kernel': 'linear'}
0.663 (+/-0.316) for {'C': 100.0, 'kernel': 'linear'}
0.613 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
0.588 (+/-0.233) for {'C': 10000.0, 'kernel': 'linear'}
0.588 (+/-0.233) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.08 0.17 0.11 6
1 0.99 0.98 0.99 623
avg / total 0.98 0.97 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6868579437711271
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.698 (+/-0.383) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.629 (+/-0.329) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.714 (+/-0.359) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.644 (+/-0.389) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.631 (+/-0.327) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.723 (+/-0.358) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.661 (+/-0.384) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.635 (+/-0.326) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.723 (+/-0.358) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.660 (+/-0.385) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.656 (+/-0.312) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.635 (+/-0.326) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.747 (+/-0.502) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.797 (+/-0.492) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.702 (+/-0.355) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.660 (+/-0.385) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.656 (+/-0.312) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.635 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.024) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.797 (+/-0.492) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.654 (+/-0.292) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.659 (+/-0.386) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.656 (+/-0.312) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.614 (+/-0.327) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.635 (+/-0.326) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.699 (+/-0.797) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.681 (+/-0.372) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.642 (+/-0.302) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.629 (+/-0.311) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.637 (+/-0.307) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.614 (+/-0.327) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.635 (+/-0.326) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.598 (+/-0.745) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.656 (+/-0.367) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.615 (+/-0.307) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.657 (+/-0.303) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.624 (+/-0.319) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.614 (+/-0.327) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.635 (+/-0.326) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 1.0, 'kernel': 'linear'}
0.673 (+/-0.377) for {'C': 10.0, 'kernel': 'linear'}
0.637 (+/-0.310) for {'C': 100.0, 'kernel': 'linear'}
0.622 (+/-0.319) for {'C': 1000.0, 'kernel': 'linear'}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'linear'}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.004) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.666 (+/-0.315) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.682 (+/-0.295) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.611 (+/-0.242) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.682 (+/-0.295) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.661 (+/-0.314) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.588 (+/-0.232) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.682 (+/-0.295) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.660 (+/-0.314) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.639 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.617 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.642 (+/-0.236) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.682 (+/-0.294) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.660 (+/-0.314) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.639 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.588 (+/-0.233) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.523 (+/-0.138) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.642 (+/-0.236) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.680 (+/-0.290) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.659 (+/-0.314) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.664 (+/-0.318) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.588 (+/-0.232) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.587 (+/-0.234) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.238) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.642 (+/-0.236) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.641 (+/-0.235) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.676 (+/-0.293) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.661 (+/-0.314) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.638 (+/-0.238) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.587 (+/-0.233) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.587 (+/-0.234) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.238) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.561 (+/-0.310) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.640 (+/-0.233) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.657 (+/-0.311) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.687 (+/-0.298) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.612 (+/-0.242) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.587 (+/-0.233) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.587 (+/-0.234) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.238) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.663 (+/-0.316) for {'C': 10.0, 'kernel': 'linear'}
0.662 (+/-0.315) for {'C': 100.0, 'kernel': 'linear'}
0.613 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
0.588 (+/-0.233) for {'C': 10000.0, 'kernel': 'linear'}
0.588 (+/-0.233) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.07 0.17 0.10 6
1 0.99 0.98 0.99 623
avg / total 0.98 0.97 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6868579437711271
发现最优参数C为原先的最大/最小值,直接重新设置超参。
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.772 (+/-0.473) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.748 (+/-0.449) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.747 (+/-0.502) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.747 (+/-0.502) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.747 (+/-0.502) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.747 (+/-0.502) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.772 (+/-0.473) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.747 (+/-0.449) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.723 (+/-0.423) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.723 (+/-0.423) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.698 (+/-0.383) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.706 (+/-0.383) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.706 (+/-0.383) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.798 (+/-0.437) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.722 (+/-0.417) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.689 (+/-0.382) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.698 (+/-0.383) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.689 (+/-0.375) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.685 (+/-0.376) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.683 (+/-0.378) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.644 (+/-0.319) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.638 (+/-0.310) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.644 (+/-0.309) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.656 (+/-0.312) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.676 (+/-0.294) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.697 (+/-0.438) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.660 (+/-0.327) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.629 (+/-0.313) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.630 (+/-0.312) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.631 (+/-0.310) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.644 (+/-0.298) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.664 (+/-0.298) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.624 (+/-0.318) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.639 (+/-0.309) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.637 (+/-0.307) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.648 (+/-0.306) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.673 (+/-0.377) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.649 (+/-0.323) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.626 (+/-0.314) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.622 (+/-0.319) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.657 (+/-0.303) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.634 (+/-0.310) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.639 (+/-0.327) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.649 (+/-0.314) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.638 (+/-0.309) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.624 (+/-0.319) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.648 (+/-0.306) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.665 (+/-0.376) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.649 (+/-0.323) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.626 (+/-0.314) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.623 (+/-0.319) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.659 (+/-0.301) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.632 (+/-0.310) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.647 (+/-0.341) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.649 (+/-0.314) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.646 (+/-0.317) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.624 (+/-0.319) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.648 (+/-0.306) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.665 (+/-0.376) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.649 (+/-0.323) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.626 (+/-0.314) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.623 (+/-0.319) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.659 (+/-0.301) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.632 (+/-0.310) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.647 (+/-0.341) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.649 (+/-0.314) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.646 (+/-0.317) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.624 (+/-0.319) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.648 (+/-0.306) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.665 (+/-0.376) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.649 (+/-0.323) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.626 (+/-0.314) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.623 (+/-0.319) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.659 (+/-0.301) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.632 (+/-0.310) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.647 (+/-0.341) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.649 (+/-0.314) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.646 (+/-0.317) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.624 (+/-0.319) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.648 (+/-0.306) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.665 (+/-0.376) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.649 (+/-0.323) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.626 (+/-0.314) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.623 (+/-0.319) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.659 (+/-0.301) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.632 (+/-0.310) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.647 (+/-0.341) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.649 (+/-0.314) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.646 (+/-0.317) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.624 (+/-0.319) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.648 (+/-0.306) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.665 (+/-0.376) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.649 (+/-0.323) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.626 (+/-0.314) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.623 (+/-0.319) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.659 (+/-0.301) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.632 (+/-0.310) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.647 (+/-0.341) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.649 (+/-0.314) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.646 (+/-0.317) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.624 (+/-0.319) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'linear'}
0.706 (+/-0.383) for {'C': 10.0, 'kernel': 'linear'}
0.641 (+/-0.312) for {'C': 100.0, 'kernel': 'linear'}
0.622 (+/-0.319) for {'C': 1000.0, 'kernel': 'linear'}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'linear'}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.642 (+/-0.236) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.666 (+/-0.316) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.617 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.617 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.617 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.617 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.642 (+/-0.236) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.641 (+/-0.236) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.666 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.666 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.666 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.666 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.666 (+/-0.316) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.683 (+/-0.296) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.641 (+/-0.235) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.666 (+/-0.315) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.666 (+/-0.316) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.666 (+/-0.316) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.665 (+/-0.315) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.664 (+/-0.316) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.662 (+/-0.317) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.662 (+/-0.316) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.663 (+/-0.317) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.664 (+/-0.318) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.681 (+/-0.295) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.639 (+/-0.329) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.665 (+/-0.316) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.660 (+/-0.316) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.660 (+/-0.315) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.661 (+/-0.315) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.687 (+/-0.298) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.688 (+/-0.298) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.614 (+/-0.238) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.638 (+/-0.237) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.638 (+/-0.238) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.664 (+/-0.315) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.662 (+/-0.320) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.664 (+/-0.317) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.658 (+/-0.315) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.634 (+/-0.327) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.687 (+/-0.298) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.662 (+/-0.316) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.638 (+/-0.327) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.639 (+/-0.238) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.638 (+/-0.236) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.612 (+/-0.242) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.664 (+/-0.315) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.661 (+/-0.320) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.663 (+/-0.317) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.658 (+/-0.315) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.635 (+/-0.326) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.687 (+/-0.297) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.662 (+/-0.315) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.639 (+/-0.328) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.639 (+/-0.238) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.663 (+/-0.316) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.612 (+/-0.242) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.664 (+/-0.315) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.661 (+/-0.320) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.663 (+/-0.317) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.658 (+/-0.315) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.635 (+/-0.326) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.687 (+/-0.297) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.662 (+/-0.315) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.639 (+/-0.328) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.639 (+/-0.238) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.663 (+/-0.316) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.612 (+/-0.242) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.664 (+/-0.315) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.661 (+/-0.320) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.663 (+/-0.317) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.658 (+/-0.315) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.635 (+/-0.326) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.687 (+/-0.297) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.662 (+/-0.315) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.639 (+/-0.328) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.639 (+/-0.238) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.663 (+/-0.316) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.612 (+/-0.242) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.664 (+/-0.315) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.661 (+/-0.320) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.663 (+/-0.317) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.658 (+/-0.315) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.635 (+/-0.326) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.687 (+/-0.297) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.662 (+/-0.315) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.639 (+/-0.328) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.639 (+/-0.238) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.663 (+/-0.316) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.612 (+/-0.242) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.664 (+/-0.315) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.661 (+/-0.320) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.664 (+/-0.317) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.658 (+/-0.315) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.635 (+/-0.326) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.687 (+/-0.297) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.662 (+/-0.315) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.639 (+/-0.328) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.639 (+/-0.238) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.663 (+/-0.316) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.612 (+/-0.242) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'linear'}
0.666 (+/-0.315) for {'C': 10.0, 'kernel': 'linear'}
0.663 (+/-0.316) for {'C': 100.0, 'kernel': 'linear'}
0.613 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
0.588 (+/-0.233) for {'C': 10000.0, 'kernel': 'linear'}
0.588 (+/-0.233) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.11 0.17 0.13 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6879802539368455
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.797 (+/-0.492) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.748 (+/-0.449) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.698 (+/-0.383) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.723 (+/-0.358) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.797 (+/-0.492) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.797 (+/-0.492) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.797 (+/-0.492) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.748 (+/-0.449) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.689 (+/-0.382) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.702 (+/-0.355) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.673 (+/-0.330) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.676 (+/-0.370) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.669 (+/-0.373) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.665 (+/-0.381) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.660 (+/-0.385) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.654 (+/-0.292) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.673 (+/-0.330) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.656 (+/-0.312) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.659 (+/-0.375) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.627 (+/-0.314) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.659 (+/-0.386) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.660 (+/-0.385) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.632 (+/-0.311) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.634 (+/-0.309) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.644 (+/-0.309) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.656 (+/-0.312) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.642 (+/-0.302) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.656 (+/-0.390) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.622 (+/-0.313) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.624 (+/-0.316) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.630 (+/-0.312) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.629 (+/-0.311) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.644 (+/-0.298) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.664 (+/-0.298) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.624 (+/-0.318) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.639 (+/-0.309) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.637 (+/-0.307) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.615 (+/-0.307) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.646 (+/-0.387) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.615 (+/-0.307) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.624 (+/-0.315) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.622 (+/-0.319) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.657 (+/-0.303) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.634 (+/-0.310) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.639 (+/-0.327) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.649 (+/-0.314) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.638 (+/-0.309) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.624 (+/-0.319) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.615 (+/-0.307) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.646 (+/-0.387) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.615 (+/-0.307) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.624 (+/-0.315) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.623 (+/-0.319) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.659 (+/-0.301) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.632 (+/-0.310) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.647 (+/-0.341) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.649 (+/-0.314) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.646 (+/-0.317) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.624 (+/-0.319) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.615 (+/-0.307) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.646 (+/-0.387) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.615 (+/-0.307) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.624 (+/-0.315) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.623 (+/-0.319) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.659 (+/-0.301) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.632 (+/-0.310) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.647 (+/-0.341) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.649 (+/-0.314) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.646 (+/-0.317) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.624 (+/-0.319) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.615 (+/-0.307) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.646 (+/-0.387) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.615 (+/-0.307) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.624 (+/-0.315) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.623 (+/-0.319) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.659 (+/-0.301) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.632 (+/-0.310) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.647 (+/-0.341) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.649 (+/-0.314) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.646 (+/-0.317) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.624 (+/-0.319) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.615 (+/-0.307) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.646 (+/-0.387) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.615 (+/-0.307) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.624 (+/-0.315) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.623 (+/-0.319) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.659 (+/-0.301) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.632 (+/-0.310) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.647 (+/-0.341) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.649 (+/-0.314) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.646 (+/-0.317) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.624 (+/-0.319) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.615 (+/-0.307) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.646 (+/-0.387) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.615 (+/-0.307) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.624 (+/-0.315) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.623 (+/-0.319) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.659 (+/-0.301) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.632 (+/-0.310) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.647 (+/-0.341) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.649 (+/-0.314) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.646 (+/-0.317) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.624 (+/-0.319) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 1.0, 'kernel': 'linear'}
0.673 (+/-0.377) for {'C': 10.0, 'kernel': 'linear'}
0.637 (+/-0.310) for {'C': 100.0, 'kernel': 'linear'}
0.622 (+/-0.319) for {'C': 1000.0, 'kernel': 'linear'}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'linear'}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.642 (+/-0.236) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.666 (+/-0.316) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.666 (+/-0.315) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.682 (+/-0.295) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.642 (+/-0.236) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.642 (+/-0.236) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.642 (+/-0.236) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.666 (+/-0.316) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.666 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.682 (+/-0.294) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.665 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.665 (+/-0.314) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.664 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.662 (+/-0.316) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.660 (+/-0.314) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.680 (+/-0.290) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.665 (+/-0.315) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.665 (+/-0.315) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.662 (+/-0.315) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.659 (+/-0.314) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.659 (+/-0.314) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.660 (+/-0.315) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.661 (+/-0.315) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.662 (+/-0.315) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.663 (+/-0.317) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.664 (+/-0.318) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.676 (+/-0.293) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.654 (+/-0.316) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.657 (+/-0.316) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.656 (+/-0.315) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.660 (+/-0.316) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.661 (+/-0.314) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.687 (+/-0.298) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.688 (+/-0.298) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.614 (+/-0.238) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.638 (+/-0.237) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.638 (+/-0.238) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.657 (+/-0.311) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.652 (+/-0.314) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.655 (+/-0.317) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.657 (+/-0.315) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.634 (+/-0.327) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.687 (+/-0.298) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.662 (+/-0.316) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.638 (+/-0.327) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.639 (+/-0.238) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.638 (+/-0.236) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.612 (+/-0.242) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.657 (+/-0.312) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.652 (+/-0.313) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.654 (+/-0.318) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.657 (+/-0.314) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.635 (+/-0.326) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.687 (+/-0.297) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.662 (+/-0.315) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.639 (+/-0.328) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.639 (+/-0.238) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.663 (+/-0.316) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.612 (+/-0.242) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.657 (+/-0.312) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.652 (+/-0.314) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.654 (+/-0.318) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.657 (+/-0.314) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.635 (+/-0.326) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.687 (+/-0.297) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.662 (+/-0.315) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.639 (+/-0.328) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.639 (+/-0.238) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.663 (+/-0.316) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.612 (+/-0.242) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.657 (+/-0.312) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.652 (+/-0.314) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.654 (+/-0.318) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.657 (+/-0.314) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.635 (+/-0.326) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.687 (+/-0.297) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.662 (+/-0.315) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.639 (+/-0.328) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.639 (+/-0.238) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.663 (+/-0.316) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.612 (+/-0.242) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.657 (+/-0.312) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.652 (+/-0.314) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.654 (+/-0.318) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.657 (+/-0.314) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.635 (+/-0.326) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.687 (+/-0.297) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.662 (+/-0.315) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.639 (+/-0.328) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.639 (+/-0.238) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.663 (+/-0.316) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.612 (+/-0.242) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.657 (+/-0.312) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.652 (+/-0.314) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.654 (+/-0.318) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.657 (+/-0.314) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.635 (+/-0.326) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.687 (+/-0.297) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.662 (+/-0.315) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.639 (+/-0.328) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.639 (+/-0.238) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.663 (+/-0.316) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.612 (+/-0.242) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.663 (+/-0.316) for {'C': 10.0, 'kernel': 'linear'}
0.662 (+/-0.315) for {'C': 100.0, 'kernel': 'linear'}
0.613 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
0.588 (+/-0.233) for {'C': 10000.0, 'kernel': 'linear'}
0.588 (+/-0.233) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.11 0.17 0.13 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6879802539368455
循环迭代之前,delta is: [9.00000000e+07 1.51188643e-05]
执行tunemodel()函数前,使用的grid search parameter是:
[{'C': array([ 1000000. , 1584893.19246111, 2511886.43150958,
3981071.70553497, 6309573.44480193, 9999999.99999999,
15848931.92461113, 25118864.31509581, 39810717.05534975,
63095734.44801924, 99999999.99999991]), 'kernel': ['rbf'], 'gamma': array([1.58489319e-05, 1.73780083e-05, 1.90546072e-05, 2.08929613e-05,
2.29086765e-05, 2.51188643e-05, 2.75422870e-05, 3.01995172e-05,
3.31131121e-05, 3.63078055e-05, 3.98107171e-05])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.683 (+/-0.378) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.691 (+/-0.387) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.7378008287493767e-05}
0.658 (+/-0.329) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.905460717963247e-05}
0.660 (+/-0.327) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.0892961308540406e-05}
0.658 (+/-0.329) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.644 (+/-0.319) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.654 (+/-0.332) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.670 (+/-0.381) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.0199517204020175e-05}
0.646 (+/-0.317) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.3113112148259144e-05}
0.646 (+/-0.317) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.630780547701015e-05}
0.638 (+/-0.310) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.656 (+/-0.330) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.654 (+/-0.332) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.7378008287493767e-05}
0.643 (+/-0.320) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.905460717963247e-05}
0.646 (+/-0.317) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.0892961308540406e-05}
0.649 (+/-0.314) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.634 (+/-0.311) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.639 (+/-0.314) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.636 (+/-0.309) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.0199517204020175e-05}
0.638 (+/-0.310) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.3113112148259144e-05}
0.643 (+/-0.306) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.630780547701015e-05}
0.639 (+/-0.307) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.639 (+/-0.309) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.644 (+/-0.309) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.7378008287493767e-05}
0.635 (+/-0.312) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.905460717963247e-05}
0.633 (+/-0.309) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.0892961308540406e-05}
0.641 (+/-0.307) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.634 (+/-0.311) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.637 (+/-0.310) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.649 (+/-0.314) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.0199517204020175e-05}
0.636 (+/-0.309) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.3113112148259144e-05}
0.649 (+/-0.314) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.630780547701015e-05}
0.641 (+/-0.307) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.637 (+/-0.308) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.641 (+/-0.311) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.7378008287493767e-05}
0.634 (+/-0.308) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.905460717963247e-05}
0.631 (+/-0.310) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 2.0892961308540406e-05}
0.635 (+/-0.308) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.634 (+/-0.311) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.646 (+/-0.317) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.674 (+/-0.301) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.0199517204020175e-05}
0.639 (+/-0.309) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.3113112148259144e-05}
0.648 (+/-0.316) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.630780547701015e-05}
0.641 (+/-0.307) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.641 (+/-0.298) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.643 (+/-0.310) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.7378008287493767e-05}
0.633 (+/-0.309) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.905460717963247e-05}
0.634 (+/-0.308) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.0892961308540406e-05}
0.637 (+/-0.307) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.641 (+/-0.307) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.646 (+/-0.316) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.674 (+/-0.301) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.0199517204020175e-05}
0.662 (+/-0.301) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.3113112148259144e-05}
0.641 (+/-0.307) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.630780547701015e-05}
0.639 (+/-0.309) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.644 (+/-0.298) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.648 (+/-0.316) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.7378008287493767e-05}
0.633 (+/-0.309) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.905460717963247e-05}
0.625 (+/-0.318) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.0892961308540406e-05}
0.626 (+/-0.318) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.664 (+/-0.298) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.639 (+/-0.309) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.652 (+/-0.313) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.0199517204020175e-05}
0.663 (+/-0.300) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.3113112148259144e-05}
0.649 (+/-0.314) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.630780547701015e-05}
0.624 (+/-0.318) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.635 (+/-0.308) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.649 (+/-0.314) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.7378008287493767e-05}
0.637 (+/-0.307) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.905460717963247e-05}
0.628 (+/-0.318) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.0892961308540406e-05}
0.624 (+/-0.319) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.669 (+/-0.298) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.664 (+/-0.299) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.652 (+/-0.313) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 3.0199517204020175e-05}
0.645 (+/-0.317) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 3.3113112148259144e-05}
0.639 (+/-0.309) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 3.630780547701015e-05}
0.624 (+/-0.318) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.635 (+/-0.308) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.660 (+/-0.327) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.7378008287493767e-05}
0.627 (+/-0.318) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.905460717963247e-05}
0.643 (+/-0.306) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.0892961308540406e-05}
0.631 (+/-0.319) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.669 (+/-0.298) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.660 (+/-0.327) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.656 (+/-0.312) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 3.0199517204020175e-05}
0.618 (+/-0.322) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 3.3113112148259144e-05}
0.647 (+/-0.315) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 3.630780547701015e-05}
0.641 (+/-0.307) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.634 (+/-0.310) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.681 (+/-0.373) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.7378008287493767e-05}
0.622 (+/-0.319) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.905460717963247e-05}
0.656 (+/-0.312) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 2.0892961308540406e-05}
0.624 (+/-0.318) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.634 (+/-0.321) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.643 (+/-0.306) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.647 (+/-0.308) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 3.0199517204020175e-05}
0.618 (+/-0.322) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 3.3113112148259144e-05}
0.639 (+/-0.309) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 3.630780547701015e-05}
0.641 (+/-0.307) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.635 (+/-0.310) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.681 (+/-0.373) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.7378008287493767e-05}
0.622 (+/-0.319) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.905460717963247e-05}
0.631 (+/-0.319) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 2.0892961308540406e-05}
0.622 (+/-0.319) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.634 (+/-0.321) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.643 (+/-0.306) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.641 (+/-0.307) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 3.0199517204020175e-05}
0.618 (+/-0.322) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 3.3113112148259144e-05}
0.639 (+/-0.309) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 3.630780547701015e-05}
0.641 (+/-0.307) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.634 (+/-0.310) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.681 (+/-0.373) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.7378008287493767e-05}
0.624 (+/-0.319) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.905460717963247e-05}
0.626 (+/-0.318) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 2.0892961308540406e-05}
0.624 (+/-0.318) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.639 (+/-0.327) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.643 (+/-0.306) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.641 (+/-0.307) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.0199517204020175e-05}
0.618 (+/-0.322) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.3113112148259144e-05}
0.639 (+/-0.309) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.630780547701015e-05}
0.649 (+/-0.314) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'linear'}
0.706 (+/-0.383) for {'C': 10.0, 'kernel': 'linear'}
0.641 (+/-0.312) for {'C': 100.0, 'kernel': 'linear'}
0.622 (+/-0.319) for {'C': 1000.0, 'kernel': 'linear'}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'linear'}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.664 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.664 (+/-0.317) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.7378008287493767e-05}
0.664 (+/-0.317) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.905460717963247e-05}
0.664 (+/-0.317) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.0892961308540406e-05}
0.663 (+/-0.318) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.662 (+/-0.317) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.663 (+/-0.318) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.663 (+/-0.319) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.0199517204020175e-05}
0.663 (+/-0.317) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.3113112148259144e-05}
0.663 (+/-0.318) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.630780547701015e-05}
0.662 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.663 (+/-0.318) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.663 (+/-0.318) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.7378008287493767e-05}
0.662 (+/-0.316) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.905460717963247e-05}
0.663 (+/-0.317) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.0892961308540406e-05}
0.663 (+/-0.318) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.662 (+/-0.315) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.662 (+/-0.316) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.662 (+/-0.315) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.0199517204020175e-05}
0.663 (+/-0.316) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.3113112148259144e-05}
0.663 (+/-0.317) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.630780547701015e-05}
0.663 (+/-0.316) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.662 (+/-0.317) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.663 (+/-0.316) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.7378008287493767e-05}
0.662 (+/-0.316) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.905460717963247e-05}
0.662 (+/-0.314) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.0892961308540406e-05}
0.638 (+/-0.238) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.662 (+/-0.315) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.662 (+/-0.316) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.663 (+/-0.317) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.0199517204020175e-05}
0.663 (+/-0.315) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.3113112148259144e-05}
0.663 (+/-0.318) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.630780547701015e-05}
0.663 (+/-0.315) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.662 (+/-0.316) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.663 (+/-0.316) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.7378008287493767e-05}
0.662 (+/-0.315) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.905460717963247e-05}
0.662 (+/-0.314) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 2.0892961308540406e-05}
0.638 (+/-0.237) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.662 (+/-0.315) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.663 (+/-0.316) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.688 (+/-0.300) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.0199517204020175e-05}
0.663 (+/-0.315) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.3113112148259144e-05}
0.663 (+/-0.317) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.630780547701015e-05}
0.663 (+/-0.315) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.686 (+/-0.296) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.663 (+/-0.316) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.7378008287493767e-05}
0.662 (+/-0.315) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.905460717963247e-05}
0.662 (+/-0.314) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.0892961308540406e-05}
0.638 (+/-0.237) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.663 (+/-0.316) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.638 (+/-0.237) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.688 (+/-0.300) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.0199517204020175e-05}
0.663 (+/-0.224) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.3113112148259144e-05}
0.638 (+/-0.237) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.630780547701015e-05}
0.639 (+/-0.236) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.687 (+/-0.298) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.663 (+/-0.317) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.7378008287493767e-05}
0.662 (+/-0.314) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.905460717963247e-05}
0.637 (+/-0.324) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.0892961308540406e-05}
0.613 (+/-0.240) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.688 (+/-0.298) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.638 (+/-0.237) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.663 (+/-0.317) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.0199517204020175e-05}
0.664 (+/-0.223) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.3113112148259144e-05}
0.639 (+/-0.237) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.630780547701015e-05}
0.614 (+/-0.238) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.662 (+/-0.316) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.663 (+/-0.317) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.7378008287493767e-05}
0.662 (+/-0.316) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.905460717963247e-05}
0.637 (+/-0.326) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.0892961308540406e-05}
0.613 (+/-0.241) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.689 (+/-0.298) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.664 (+/-0.224) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.664 (+/-0.317) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 3.0199517204020175e-05}
0.639 (+/-0.236) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 3.3113112148259144e-05}
0.639 (+/-0.236) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 3.630780547701015e-05}
0.614 (+/-0.239) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.661 (+/-0.317) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.664 (+/-0.318) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.7378008287493767e-05}
0.637 (+/-0.326) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.905460717963247e-05}
0.638 (+/-0.240) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.0892961308540406e-05}
0.613 (+/-0.241) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.688 (+/-0.300) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.664 (+/-0.317) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.664 (+/-0.317) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 3.0199517204020175e-05}
0.613 (+/-0.236) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 3.3113112148259144e-05}
0.639 (+/-0.236) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 3.630780547701015e-05}
0.638 (+/-0.237) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.661 (+/-0.316) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.664 (+/-0.319) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.7378008287493767e-05}
0.612 (+/-0.241) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.905460717963247e-05}
0.637 (+/-0.242) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 2.0892961308540406e-05}
0.613 (+/-0.240) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.638 (+/-0.326) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.639 (+/-0.237) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.664 (+/-0.316) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 3.0199517204020175e-05}
0.612 (+/-0.236) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 3.3113112148259144e-05}
0.639 (+/-0.236) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 3.630780547701015e-05}
0.638 (+/-0.237) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.661 (+/-0.317) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.664 (+/-0.319) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.7378008287493767e-05}
0.612 (+/-0.241) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.905460717963247e-05}
0.612 (+/-0.243) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 2.0892961308540406e-05}
0.613 (+/-0.239) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.638 (+/-0.326) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.639 (+/-0.236) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.663 (+/-0.315) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 3.0199517204020175e-05}
0.612 (+/-0.236) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 3.3113112148259144e-05}
0.638 (+/-0.237) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 3.630780547701015e-05}
0.638 (+/-0.237) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.662 (+/-0.316) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.664 (+/-0.319) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.7378008287493767e-05}
0.612 (+/-0.241) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.905460717963247e-05}
0.612 (+/-0.242) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 2.0892961308540406e-05}
0.613 (+/-0.240) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.638 (+/-0.327) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.639 (+/-0.236) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.663 (+/-0.315) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.0199517204020175e-05}
0.612 (+/-0.236) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.3113112148259144e-05}
0.638 (+/-0.237) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.630780547701015e-05}
0.639 (+/-0.238) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'linear'}
0.666 (+/-0.315) for {'C': 10.0, 'kernel': 'linear'}
0.663 (+/-0.316) for {'C': 100.0, 'kernel': 'linear'}
0.613 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
0.588 (+/-0.233) for {'C': 10000.0, 'kernel': 'linear'}
0.588 (+/-0.233) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.11 0.17 0.13 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6886212795778712
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.660 (+/-0.385) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.665 (+/-0.382) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.7378008287493767e-05}
0.626 (+/-0.314) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.905460717963247e-05}
0.627 (+/-0.312) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.0892961308540406e-05}
0.633 (+/-0.309) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.632 (+/-0.311) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.631 (+/-0.311) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.632 (+/-0.310) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.0199517204020175e-05}
0.641 (+/-0.311) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.3113112148259144e-05}
0.638 (+/-0.310) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.630780547701015e-05}
0.634 (+/-0.309) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.634 (+/-0.308) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.635 (+/-0.310) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.7378008287493767e-05}
0.626 (+/-0.314) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.905460717963247e-05}
0.631 (+/-0.310) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.0892961308540406e-05}
0.639 (+/-0.307) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.632 (+/-0.311) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.630 (+/-0.312) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.636 (+/-0.309) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.0199517204020175e-05}
0.638 (+/-0.310) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.3113112148259144e-05}
0.643 (+/-0.306) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.630780547701015e-05}
0.639 (+/-0.307) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.637 (+/-0.308) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.641 (+/-0.307) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.7378008287493767e-05}
0.629 (+/-0.312) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.905460717963247e-05}
0.633 (+/-0.309) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.0892961308540406e-05}
0.637 (+/-0.307) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.634 (+/-0.311) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.637 (+/-0.310) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.649 (+/-0.314) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.0199517204020175e-05}
0.636 (+/-0.309) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.3113112148259144e-05}
0.649 (+/-0.314) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.630780547701015e-05}
0.641 (+/-0.307) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.637 (+/-0.308) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.641 (+/-0.311) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.7378008287493767e-05}
0.634 (+/-0.308) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.905460717963247e-05}
0.631 (+/-0.310) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 2.0892961308540406e-05}
0.635 (+/-0.308) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.634 (+/-0.311) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.646 (+/-0.317) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.674 (+/-0.301) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.0199517204020175e-05}
0.639 (+/-0.309) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.3113112148259144e-05}
0.648 (+/-0.316) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.630780547701015e-05}
0.641 (+/-0.307) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.641 (+/-0.298) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.643 (+/-0.310) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.7378008287493767e-05}
0.633 (+/-0.309) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.905460717963247e-05}
0.634 (+/-0.308) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.0892961308540406e-05}
0.637 (+/-0.307) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.641 (+/-0.307) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.646 (+/-0.316) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.674 (+/-0.301) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.0199517204020175e-05}
0.662 (+/-0.301) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.3113112148259144e-05}
0.641 (+/-0.307) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.630780547701015e-05}
0.639 (+/-0.309) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.644 (+/-0.298) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.648 (+/-0.316) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.7378008287493767e-05}
0.633 (+/-0.309) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.905460717963247e-05}
0.625 (+/-0.318) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.0892961308540406e-05}
0.626 (+/-0.318) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.664 (+/-0.298) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.639 (+/-0.309) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.652 (+/-0.313) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.0199517204020175e-05}
0.663 (+/-0.300) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.3113112148259144e-05}
0.649 (+/-0.314) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.630780547701015e-05}
0.624 (+/-0.318) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.635 (+/-0.308) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.649 (+/-0.314) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.7378008287493767e-05}
0.637 (+/-0.307) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.905460717963247e-05}
0.628 (+/-0.318) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.0892961308540406e-05}
0.624 (+/-0.319) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.669 (+/-0.298) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.664 (+/-0.299) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.652 (+/-0.313) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 3.0199517204020175e-05}
0.645 (+/-0.317) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 3.3113112148259144e-05}
0.639 (+/-0.309) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 3.630780547701015e-05}
0.624 (+/-0.318) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.635 (+/-0.308) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.660 (+/-0.327) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.7378008287493767e-05}
0.627 (+/-0.318) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.905460717963247e-05}
0.643 (+/-0.306) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.0892961308540406e-05}
0.631 (+/-0.319) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.669 (+/-0.298) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.660 (+/-0.327) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.656 (+/-0.312) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 3.0199517204020175e-05}
0.618 (+/-0.322) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 3.3113112148259144e-05}
0.647 (+/-0.315) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 3.630780547701015e-05}
0.641 (+/-0.307) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.634 (+/-0.310) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.681 (+/-0.373) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.7378008287493767e-05}
0.622 (+/-0.319) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.905460717963247e-05}
0.656 (+/-0.312) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 2.0892961308540406e-05}
0.624 (+/-0.318) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.634 (+/-0.321) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.643 (+/-0.306) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.647 (+/-0.308) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 3.0199517204020175e-05}
0.618 (+/-0.322) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 3.3113112148259144e-05}
0.639 (+/-0.309) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 3.630780547701015e-05}
0.641 (+/-0.307) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.635 (+/-0.310) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.681 (+/-0.373) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.7378008287493767e-05}
0.622 (+/-0.319) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.905460717963247e-05}
0.631 (+/-0.319) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 2.0892961308540406e-05}
0.622 (+/-0.319) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.634 (+/-0.321) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.643 (+/-0.306) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.641 (+/-0.307) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 3.0199517204020175e-05}
0.618 (+/-0.322) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 3.3113112148259144e-05}
0.639 (+/-0.309) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 3.630780547701015e-05}
0.641 (+/-0.307) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.634 (+/-0.310) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.681 (+/-0.373) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.7378008287493767e-05}
0.624 (+/-0.319) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.905460717963247e-05}
0.626 (+/-0.318) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 2.0892961308540406e-05}
0.624 (+/-0.318) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.639 (+/-0.327) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.643 (+/-0.306) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.641 (+/-0.307) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.0199517204020175e-05}
0.618 (+/-0.322) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.3113112148259144e-05}
0.639 (+/-0.309) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.630780547701015e-05}
0.649 (+/-0.314) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 1.0, 'kernel': 'linear'}
0.673 (+/-0.377) for {'C': 10.0, 'kernel': 'linear'}
0.637 (+/-0.310) for {'C': 100.0, 'kernel': 'linear'}
0.622 (+/-0.319) for {'C': 1000.0, 'kernel': 'linear'}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'linear'}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.20 0.17 0.18 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.660 (+/-0.315) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.661 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.7378008287493767e-05}
0.659 (+/-0.312) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.905460717963247e-05}
0.660 (+/-0.313) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.0892961308540406e-05}
0.661 (+/-0.315) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.661 (+/-0.315) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.661 (+/-0.315) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.662 (+/-0.315) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.0199517204020175e-05}
0.663 (+/-0.317) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.3113112148259144e-05}
0.662 (+/-0.317) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.630780547701015e-05}
0.662 (+/-0.315) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.661 (+/-0.316) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.662 (+/-0.316) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.7378008287493767e-05}
0.660 (+/-0.311) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.905460717963247e-05}
0.661 (+/-0.314) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.0892961308540406e-05}
0.662 (+/-0.317) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.662 (+/-0.314) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.661 (+/-0.314) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.662 (+/-0.315) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.0199517204020175e-05}
0.663 (+/-0.316) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.3113112148259144e-05}
0.663 (+/-0.317) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.630780547701015e-05}
0.663 (+/-0.316) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.662 (+/-0.317) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.663 (+/-0.316) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.7378008287493767e-05}
0.661 (+/-0.314) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.905460717963247e-05}
0.662 (+/-0.315) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.0892961308540406e-05}
0.638 (+/-0.238) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.662 (+/-0.315) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.662 (+/-0.316) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.663 (+/-0.317) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.0199517204020175e-05}
0.663 (+/-0.315) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.3113112148259144e-05}
0.663 (+/-0.318) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.630780547701015e-05}
0.663 (+/-0.315) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.662 (+/-0.316) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.663 (+/-0.316) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.7378008287493767e-05}
0.662 (+/-0.315) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.905460717963247e-05}
0.662 (+/-0.314) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 2.0892961308540406e-05}
0.638 (+/-0.237) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.662 (+/-0.315) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.663 (+/-0.316) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.688 (+/-0.300) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.0199517204020175e-05}
0.663 (+/-0.315) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.3113112148259144e-05}
0.663 (+/-0.317) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.630780547701015e-05}
0.663 (+/-0.315) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.686 (+/-0.296) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.663 (+/-0.316) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.7378008287493767e-05}
0.662 (+/-0.315) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.905460717963247e-05}
0.662 (+/-0.314) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.0892961308540406e-05}
0.638 (+/-0.237) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.663 (+/-0.316) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.638 (+/-0.237) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.688 (+/-0.300) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.0199517204020175e-05}
0.663 (+/-0.224) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.3113112148259144e-05}
0.638 (+/-0.237) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.630780547701015e-05}
0.639 (+/-0.236) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.687 (+/-0.298) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.663 (+/-0.317) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.7378008287493767e-05}
0.662 (+/-0.314) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.905460717963247e-05}
0.637 (+/-0.324) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.0892961308540406e-05}
0.613 (+/-0.240) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.688 (+/-0.298) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.638 (+/-0.237) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.663 (+/-0.317) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.0199517204020175e-05}
0.664 (+/-0.223) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.3113112148259144e-05}
0.639 (+/-0.237) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.630780547701015e-05}
0.614 (+/-0.238) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.662 (+/-0.316) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.663 (+/-0.317) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.7378008287493767e-05}
0.662 (+/-0.316) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.905460717963247e-05}
0.637 (+/-0.326) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.0892961308540406e-05}
0.613 (+/-0.241) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.689 (+/-0.298) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.664 (+/-0.224) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.664 (+/-0.317) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 3.0199517204020175e-05}
0.639 (+/-0.236) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 3.3113112148259144e-05}
0.639 (+/-0.236) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 3.630780547701015e-05}
0.614 (+/-0.239) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.661 (+/-0.317) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.664 (+/-0.318) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.7378008287493767e-05}
0.637 (+/-0.326) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.905460717963247e-05}
0.638 (+/-0.240) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.0892961308540406e-05}
0.613 (+/-0.241) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.688 (+/-0.300) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.664 (+/-0.317) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.664 (+/-0.317) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 3.0199517204020175e-05}
0.613 (+/-0.236) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 3.3113112148259144e-05}
0.639 (+/-0.236) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 3.630780547701015e-05}
0.638 (+/-0.237) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.661 (+/-0.316) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.664 (+/-0.319) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.7378008287493767e-05}
0.612 (+/-0.241) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.905460717963247e-05}
0.637 (+/-0.242) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 2.0892961308540406e-05}
0.613 (+/-0.240) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.638 (+/-0.326) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.639 (+/-0.237) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.664 (+/-0.316) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 3.0199517204020175e-05}
0.612 (+/-0.236) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 3.3113112148259144e-05}
0.639 (+/-0.236) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 3.630780547701015e-05}
0.638 (+/-0.237) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.661 (+/-0.317) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.664 (+/-0.319) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.7378008287493767e-05}
0.612 (+/-0.241) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.905460717963247e-05}
0.612 (+/-0.243) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 2.0892961308540406e-05}
0.613 (+/-0.239) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.638 (+/-0.326) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.639 (+/-0.236) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.663 (+/-0.315) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 3.0199517204020175e-05}
0.612 (+/-0.236) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 3.3113112148259144e-05}
0.638 (+/-0.237) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 3.630780547701015e-05}
0.638 (+/-0.237) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.662 (+/-0.316) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.664 (+/-0.319) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.7378008287493767e-05}
0.612 (+/-0.241) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.905460717963247e-05}
0.612 (+/-0.242) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 2.0892961308540406e-05}
0.613 (+/-0.240) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.638 (+/-0.327) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.639 (+/-0.236) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.663 (+/-0.315) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.0199517204020175e-05}
0.612 (+/-0.236) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.3113112148259144e-05}
0.638 (+/-0.237) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.630780547701015e-05}
0.639 (+/-0.238) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.663 (+/-0.316) for {'C': 10.0, 'kernel': 'linear'}
0.662 (+/-0.315) for {'C': 100.0, 'kernel': 'linear'}
0.613 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
0.588 (+/-0.233) for {'C': 10000.0, 'kernel': 'linear'}
0.588 (+/-0.233) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.11 0.17 0.13 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6886212795778712
第0轮loop中,tunemodel函数得到的最优参数是: ([{'C': array([ 9999999.99999999, 10964781.96143185, 12022644.34617414,
13182567.38556406, 14454397.70745927, 15848931.92461113,
17378008.28749376, 19054607.17963249, 20892961.30854038,
22908676.52767773, 25118864.31509581]), 'kernel': ['rbf'], 'gamma': array([2.29086765e-05, 2.33345806e-05, 2.37684029e-05, 2.42102905e-05,
2.46603934e-05, 2.51188643e-05, 2.55858589e-05, 2.60615355e-05,
2.65460556e-05, 2.70395836e-05, 2.75422870e-05])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}], {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}, 0.6886212795778712)
这是第0次迭代微调C和gamma。
第0次迭代,得到delta: [5848931.92461113 0. ]
执行tunemodel()函数前,使用的grid search parameter是:
[{'C': array([ 9999999.99999999, 10964781.96143185, 12022644.34617414,
13182567.38556406, 14454397.70745927, 15848931.92461113,
17378008.28749376, 19054607.17963249, 20892961.30854038,
22908676.52767773, 25118864.31509581]), 'kernel': ['rbf'], 'gamma': array([2.29086765e-05, 2.33345806e-05, 2.37684029e-05, 2.42102905e-05,
2.46603934e-05, 2.51188643e-05, 2.55858589e-05, 2.60615355e-05,
2.65460556e-05, 2.70395836e-05, 2.75422870e-05])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.626 (+/-0.318) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.664 (+/-0.326) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.3334580622810015e-05}
0.625 (+/-0.318) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.3768402866248774e-05}
0.640 (+/-0.313) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.4210290467361783e-05}
0.641 (+/-0.307) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.466039337234341e-05}
0.664 (+/-0.298) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.634 (+/-0.309) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5585858869056458e-05}
0.656 (+/-0.294) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.606153549998898e-05}
0.634 (+/-0.321) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.654605561975541e-05}
0.664 (+/-0.297) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.7039583641088475e-05}
0.639 (+/-0.309) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.626 (+/-0.318) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.656 (+/-0.312) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 2.3334580622810015e-05}
0.625 (+/-0.318) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 2.3768402866248774e-05}
0.645 (+/-0.318) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 2.4210290467361783e-05}
0.649 (+/-0.314) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 2.466039337234341e-05}
0.655 (+/-0.292) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.639 (+/-0.309) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 2.5585858869056458e-05}
0.658 (+/-0.292) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 2.606153549998898e-05}
0.634 (+/-0.321) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 2.654605561975541e-05}
0.668 (+/-0.295) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 2.7039583641088475e-05}
0.639 (+/-0.309) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.626 (+/-0.318) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.656 (+/-0.312) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 2.3334580622810015e-05}
0.625 (+/-0.318) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 2.3768402866248774e-05}
0.645 (+/-0.318) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 2.4210290467361783e-05}
0.649 (+/-0.314) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 2.466039337234341e-05}
0.658 (+/-0.292) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.635 (+/-0.308) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 2.5585858869056458e-05}
0.658 (+/-0.292) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 2.606153549998898e-05}
0.643 (+/-0.310) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 2.654605561975541e-05}
0.673 (+/-0.294) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 2.7039583641088475e-05}
0.639 (+/-0.309) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.626 (+/-0.318) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.656 (+/-0.312) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 2.3334580622810015e-05}
0.625 (+/-0.318) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 2.3768402866248774e-05}
0.640 (+/-0.313) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 2.4210290467361783e-05}
0.649 (+/-0.314) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 2.466039337234341e-05}
0.666 (+/-0.297) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.634 (+/-0.309) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 2.5585858869056458e-05}
0.641 (+/-0.307) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 2.606153549998898e-05}
0.632 (+/-0.309) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 2.654605561975541e-05}
0.673 (+/-0.294) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 2.7039583641088475e-05}
0.639 (+/-0.309) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.626 (+/-0.318) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.656 (+/-0.312) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 2.3334580622810015e-05}
0.625 (+/-0.318) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 2.3768402866248774e-05}
0.640 (+/-0.313) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 2.4210290467361783e-05}
0.643 (+/-0.306) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 2.466039337234341e-05}
0.669 (+/-0.298) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.634 (+/-0.309) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 2.5585858869056458e-05}
0.647 (+/-0.308) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 2.606153549998898e-05}
0.639 (+/-0.306) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 2.654605561975541e-05}
0.656 (+/-0.312) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 2.7039583641088475e-05}
0.647 (+/-0.315) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.624 (+/-0.319) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.656 (+/-0.312) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.3334580622810015e-05}
0.650 (+/-0.313) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.3768402866248774e-05}
0.666 (+/-0.302) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.4210290467361783e-05}
0.643 (+/-0.306) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.466039337234341e-05}
0.669 (+/-0.298) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.631 (+/-0.310) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.5585858869056458e-05}
0.647 (+/-0.308) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.606153549998898e-05}
0.639 (+/-0.306) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.654605561975541e-05}
0.673 (+/-0.294) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.7039583641088475e-05}
0.664 (+/-0.299) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.624 (+/-0.319) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.656 (+/-0.312) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 2.3334580622810015e-05}
0.650 (+/-0.313) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 2.3768402866248774e-05}
0.667 (+/-0.301) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 2.4210290467361783e-05}
0.641 (+/-0.307) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 2.466039337234341e-05}
0.668 (+/-0.300) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.630 (+/-0.311) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 2.5585858869056458e-05}
0.647 (+/-0.308) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 2.606153549998898e-05}
0.639 (+/-0.306) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 2.654605561975541e-05}
0.673 (+/-0.294) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 2.7039583641088475e-05}
0.664 (+/-0.299) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.631 (+/-0.319) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.656 (+/-0.312) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 2.3334580622810015e-05}
0.650 (+/-0.313) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 2.3768402866248774e-05}
0.668 (+/-0.300) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 2.4210290467361783e-05}
0.637 (+/-0.307) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 2.466039337234341e-05}
0.668 (+/-0.300) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.629 (+/-0.311) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 2.5585858869056458e-05}
0.647 (+/-0.308) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 2.606153549998898e-05}
0.639 (+/-0.306) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 2.654605561975541e-05}
0.698 (+/-0.352) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 2.7039583641088475e-05}
0.664 (+/-0.299) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.631 (+/-0.319) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.652 (+/-0.313) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 2.3334580622810015e-05}
0.650 (+/-0.313) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 2.3768402866248774e-05}
0.643 (+/-0.310) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 2.4210290467361783e-05}
0.637 (+/-0.307) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 2.466039337234341e-05}
0.668 (+/-0.300) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.629 (+/-0.311) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 2.5585858869056458e-05}
0.643 (+/-0.306) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 2.606153549998898e-05}
0.637 (+/-0.307) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 2.654605561975541e-05}
0.706 (+/-0.353) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 2.7039583641088475e-05}
0.664 (+/-0.299) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.631 (+/-0.319) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.652 (+/-0.313) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.3334580622810015e-05}
0.652 (+/-0.313) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.3768402866248774e-05}
0.639 (+/-0.309) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.4210290467361783e-05}
0.637 (+/-0.307) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.466039337234341e-05}
0.669 (+/-0.298) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.629 (+/-0.311) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.5585858869056458e-05}
0.643 (+/-0.306) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.606153549998898e-05}
0.637 (+/-0.307) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.654605561975541e-05}
0.698 (+/-0.352) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.7039583641088475e-05}
0.683 (+/-0.314) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.631 (+/-0.319) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.652 (+/-0.313) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.3334580622810015e-05}
0.627 (+/-0.318) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.3768402866248774e-05}
0.638 (+/-0.310) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.4210290467361783e-05}
0.637 (+/-0.307) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.466039337234341e-05}
0.669 (+/-0.298) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.629 (+/-0.311) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.5585858869056458e-05}
0.643 (+/-0.306) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.606153549998898e-05}
0.637 (+/-0.307) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.654605561975541e-05}
0.681 (+/-0.372) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.7039583641088475e-05}
0.660 (+/-0.327) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'linear'}
0.706 (+/-0.383) for {'C': 10.0, 'kernel': 'linear'}
0.641 (+/-0.312) for {'C': 100.0, 'kernel': 'linear'}
0.622 (+/-0.319) for {'C': 1000.0, 'kernel': 'linear'}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'linear'}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.613 (+/-0.240) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.664 (+/-0.318) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.3334580622810015e-05}
0.638 (+/-0.324) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.3768402866248774e-05}
0.662 (+/-0.316) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.4210290467361783e-05}
0.638 (+/-0.238) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.466039337234341e-05}
0.688 (+/-0.298) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.638 (+/-0.237) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5585858869056458e-05}
0.688 (+/-0.298) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.606153549998898e-05}
0.638 (+/-0.327) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.654605561975541e-05}
0.664 (+/-0.224) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.7039583641088475e-05}
0.638 (+/-0.237) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.613 (+/-0.240) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.639 (+/-0.238) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 2.3334580622810015e-05}
0.638 (+/-0.324) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 2.3768402866248774e-05}
0.663 (+/-0.317) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 2.4210290467361783e-05}
0.638 (+/-0.239) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 2.466039337234341e-05}
0.688 (+/-0.297) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.638 (+/-0.237) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 2.5585858869056458e-05}
0.688 (+/-0.298) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 2.606153549998898e-05}
0.638 (+/-0.327) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 2.654605561975541e-05}
0.664 (+/-0.225) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 2.7039583641088475e-05}
0.638 (+/-0.237) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.613 (+/-0.241) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.639 (+/-0.238) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 2.3334580622810015e-05}
0.638 (+/-0.323) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 2.3768402866248774e-05}
0.663 (+/-0.316) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 2.4210290467361783e-05}
0.638 (+/-0.239) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 2.466039337234341e-05}
0.688 (+/-0.298) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.638 (+/-0.237) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 2.5585858869056458e-05}
0.688 (+/-0.298) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 2.606153549998898e-05}
0.663 (+/-0.317) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 2.654605561975541e-05}
0.664 (+/-0.225) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 2.7039583641088475e-05}
0.638 (+/-0.237) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.613 (+/-0.241) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.639 (+/-0.239) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 2.3334580622810015e-05}
0.637 (+/-0.324) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 2.3768402866248774e-05}
0.663 (+/-0.316) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 2.4210290467361783e-05}
0.638 (+/-0.239) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 2.466039337234341e-05}
0.688 (+/-0.298) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.638 (+/-0.237) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 2.5585858869056458e-05}
0.663 (+/-0.315) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 2.606153549998898e-05}
0.637 (+/-0.238) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 2.654605561975541e-05}
0.664 (+/-0.225) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 2.7039583641088475e-05}
0.639 (+/-0.236) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.613 (+/-0.241) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.639 (+/-0.239) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 2.3334580622810015e-05}
0.637 (+/-0.324) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 2.3768402866248774e-05}
0.663 (+/-0.316) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 2.4210290467361783e-05}
0.638 (+/-0.239) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 2.466039337234341e-05}
0.689 (+/-0.298) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.638 (+/-0.237) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 2.5585858869056458e-05}
0.664 (+/-0.316) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 2.606153549998898e-05}
0.638 (+/-0.238) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 2.654605561975541e-05}
0.639 (+/-0.238) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 2.7039583641088475e-05}
0.639 (+/-0.236) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.613 (+/-0.241) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.639 (+/-0.239) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.3334580622810015e-05}
0.663 (+/-0.315) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.3768402866248774e-05}
0.688 (+/-0.299) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.4210290467361783e-05}
0.638 (+/-0.239) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.466039337234341e-05}
0.689 (+/-0.298) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.637 (+/-0.237) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.5585858869056458e-05}
0.664 (+/-0.316) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.606153549998898e-05}
0.638 (+/-0.238) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.654605561975541e-05}
0.664 (+/-0.225) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.7039583641088475e-05}
0.664 (+/-0.224) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.613 (+/-0.240) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.639 (+/-0.239) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 2.3334580622810015e-05}
0.663 (+/-0.315) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 2.3768402866248774e-05}
0.688 (+/-0.299) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 2.4210290467361783e-05}
0.638 (+/-0.238) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 2.466039337234341e-05}
0.688 (+/-0.299) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.637 (+/-0.236) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 2.5585858869056458e-05}
0.664 (+/-0.316) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 2.606153549998898e-05}
0.638 (+/-0.238) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 2.654605561975541e-05}
0.664 (+/-0.225) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 2.7039583641088475e-05}
0.664 (+/-0.224) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.613 (+/-0.241) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.639 (+/-0.239) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 2.3334580622810015e-05}
0.662 (+/-0.315) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 2.3768402866248774e-05}
0.688 (+/-0.299) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 2.4210290467361783e-05}
0.638 (+/-0.238) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 2.466039337234341e-05}
0.688 (+/-0.299) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.637 (+/-0.236) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 2.5585858869056458e-05}
0.664 (+/-0.316) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 2.606153549998898e-05}
0.638 (+/-0.238) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 2.654605561975541e-05}
0.664 (+/-0.225) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 2.7039583641088475e-05}
0.664 (+/-0.224) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.613 (+/-0.241) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.639 (+/-0.238) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 2.3334580622810015e-05}
0.663 (+/-0.315) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 2.3768402866248774e-05}
0.663 (+/-0.316) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 2.4210290467361783e-05}
0.638 (+/-0.238) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 2.466039337234341e-05}
0.688 (+/-0.299) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.637 (+/-0.236) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 2.5585858869056458e-05}
0.664 (+/-0.315) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 2.606153549998898e-05}
0.638 (+/-0.238) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 2.654605561975541e-05}
0.665 (+/-0.225) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 2.7039583641088475e-05}
0.664 (+/-0.224) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.613 (+/-0.241) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.638 (+/-0.239) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.3334580622810015e-05}
0.663 (+/-0.315) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.3768402866248774e-05}
0.663 (+/-0.316) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.4210290467361783e-05}
0.638 (+/-0.237) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.466039337234341e-05}
0.688 (+/-0.300) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.638 (+/-0.235) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.5585858869056458e-05}
0.663 (+/-0.315) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.606153549998898e-05}
0.637 (+/-0.239) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.654605561975541e-05}
0.664 (+/-0.225) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.7039583641088475e-05}
0.689 (+/-0.300) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.613 (+/-0.241) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.638 (+/-0.239) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.3334580622810015e-05}
0.638 (+/-0.324) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.3768402866248774e-05}
0.662 (+/-0.316) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.4210290467361783e-05}
0.638 (+/-0.237) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.466039337234341e-05}
0.688 (+/-0.300) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.637 (+/-0.236) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.5585858869056458e-05}
0.663 (+/-0.315) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.606153549998898e-05}
0.637 (+/-0.239) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.654605561975541e-05}
0.639 (+/-0.238) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.7039583641088475e-05}
0.664 (+/-0.317) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'linear'}
0.666 (+/-0.315) for {'C': 10.0, 'kernel': 'linear'}
0.663 (+/-0.316) for {'C': 100.0, 'kernel': 'linear'}
0.613 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
0.588 (+/-0.233) for {'C': 10000.0, 'kernel': 'linear'}
0.588 (+/-0.233) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.10 0.17 0.12 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.688941792398384
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.626 (+/-0.318) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.664 (+/-0.326) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.3334580622810015e-05}
0.625 (+/-0.318) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.3768402866248774e-05}
0.640 (+/-0.313) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.4210290467361783e-05}
0.641 (+/-0.307) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.466039337234341e-05}
0.664 (+/-0.298) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.634 (+/-0.309) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5585858869056458e-05}
0.656 (+/-0.294) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.606153549998898e-05}
0.634 (+/-0.321) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.654605561975541e-05}
0.664 (+/-0.297) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.7039583641088475e-05}
0.639 (+/-0.309) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.626 (+/-0.318) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.656 (+/-0.312) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 2.3334580622810015e-05}
0.625 (+/-0.318) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 2.3768402866248774e-05}
0.645 (+/-0.318) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 2.4210290467361783e-05}
0.649 (+/-0.314) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 2.466039337234341e-05}
0.655 (+/-0.292) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.639 (+/-0.309) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 2.5585858869056458e-05}
0.658 (+/-0.292) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 2.606153549998898e-05}
0.634 (+/-0.321) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 2.654605561975541e-05}
0.668 (+/-0.295) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 2.7039583641088475e-05}
0.639 (+/-0.309) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.626 (+/-0.318) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.656 (+/-0.312) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 2.3334580622810015e-05}
0.625 (+/-0.318) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 2.3768402866248774e-05}
0.645 (+/-0.318) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 2.4210290467361783e-05}
0.649 (+/-0.314) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 2.466039337234341e-05}
0.658 (+/-0.292) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.635 (+/-0.308) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 2.5585858869056458e-05}
0.658 (+/-0.292) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 2.606153549998898e-05}
0.643 (+/-0.310) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 2.654605561975541e-05}
0.673 (+/-0.294) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 2.7039583641088475e-05}
0.639 (+/-0.309) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.626 (+/-0.318) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.656 (+/-0.312) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 2.3334580622810015e-05}
0.625 (+/-0.318) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 2.3768402866248774e-05}
0.640 (+/-0.313) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 2.4210290467361783e-05}
0.649 (+/-0.314) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 2.466039337234341e-05}
0.666 (+/-0.297) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.634 (+/-0.309) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 2.5585858869056458e-05}
0.641 (+/-0.307) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 2.606153549998898e-05}
0.632 (+/-0.309) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 2.654605561975541e-05}
0.673 (+/-0.294) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 2.7039583641088475e-05}
0.639 (+/-0.309) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.626 (+/-0.318) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.656 (+/-0.312) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 2.3334580622810015e-05}
0.625 (+/-0.318) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 2.3768402866248774e-05}
0.640 (+/-0.313) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 2.4210290467361783e-05}
0.643 (+/-0.306) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 2.466039337234341e-05}
0.669 (+/-0.298) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.634 (+/-0.309) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 2.5585858869056458e-05}
0.647 (+/-0.308) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 2.606153549998898e-05}
0.639 (+/-0.306) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 2.654605561975541e-05}
0.656 (+/-0.312) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 2.7039583641088475e-05}
0.647 (+/-0.315) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.624 (+/-0.319) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.656 (+/-0.312) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.3334580622810015e-05}
0.650 (+/-0.313) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.3768402866248774e-05}
0.666 (+/-0.302) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.4210290467361783e-05}
0.643 (+/-0.306) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.466039337234341e-05}
0.669 (+/-0.298) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.631 (+/-0.310) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.5585858869056458e-05}
0.647 (+/-0.308) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.606153549998898e-05}
0.639 (+/-0.306) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.654605561975541e-05}
0.673 (+/-0.294) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.7039583641088475e-05}
0.664 (+/-0.299) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.624 (+/-0.319) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.656 (+/-0.312) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 2.3334580622810015e-05}
0.650 (+/-0.313) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 2.3768402866248774e-05}
0.667 (+/-0.301) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 2.4210290467361783e-05}
0.641 (+/-0.307) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 2.466039337234341e-05}
0.668 (+/-0.300) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.630 (+/-0.311) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 2.5585858869056458e-05}
0.647 (+/-0.308) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 2.606153549998898e-05}
0.639 (+/-0.306) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 2.654605561975541e-05}
0.673 (+/-0.294) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 2.7039583641088475e-05}
0.664 (+/-0.299) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.631 (+/-0.319) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.656 (+/-0.312) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 2.3334580622810015e-05}
0.650 (+/-0.313) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 2.3768402866248774e-05}
0.668 (+/-0.300) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 2.4210290467361783e-05}
0.637 (+/-0.307) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 2.466039337234341e-05}
0.668 (+/-0.300) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.629 (+/-0.311) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 2.5585858869056458e-05}
0.647 (+/-0.308) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 2.606153549998898e-05}
0.639 (+/-0.306) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 2.654605561975541e-05}
0.698 (+/-0.352) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 2.7039583641088475e-05}
0.664 (+/-0.299) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.631 (+/-0.319) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.652 (+/-0.313) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 2.3334580622810015e-05}
0.650 (+/-0.313) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 2.3768402866248774e-05}
0.643 (+/-0.310) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 2.4210290467361783e-05}
0.637 (+/-0.307) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 2.466039337234341e-05}
0.668 (+/-0.300) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.629 (+/-0.311) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 2.5585858869056458e-05}
0.643 (+/-0.306) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 2.606153549998898e-05}
0.637 (+/-0.307) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 2.654605561975541e-05}
0.706 (+/-0.353) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 2.7039583641088475e-05}
0.664 (+/-0.299) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.631 (+/-0.319) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.652 (+/-0.313) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.3334580622810015e-05}
0.652 (+/-0.313) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.3768402866248774e-05}
0.639 (+/-0.309) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.4210290467361783e-05}
0.637 (+/-0.307) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.466039337234341e-05}
0.669 (+/-0.298) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.629 (+/-0.311) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.5585858869056458e-05}
0.643 (+/-0.306) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.606153549998898e-05}
0.637 (+/-0.307) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.654605561975541e-05}
0.698 (+/-0.352) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.7039583641088475e-05}
0.683 (+/-0.314) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.631 (+/-0.319) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.652 (+/-0.313) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.3334580622810015e-05}
0.627 (+/-0.318) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.3768402866248774e-05}
0.638 (+/-0.310) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.4210290467361783e-05}
0.637 (+/-0.307) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.466039337234341e-05}
0.669 (+/-0.298) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.629 (+/-0.311) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.5585858869056458e-05}
0.643 (+/-0.306) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.606153549998898e-05}
0.637 (+/-0.307) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.654605561975541e-05}
0.681 (+/-0.372) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.7039583641088475e-05}
0.660 (+/-0.327) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 1.0, 'kernel': 'linear'}
0.673 (+/-0.377) for {'C': 10.0, 'kernel': 'linear'}
0.637 (+/-0.310) for {'C': 100.0, 'kernel': 'linear'}
0.622 (+/-0.319) for {'C': 1000.0, 'kernel': 'linear'}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'linear'}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.20 0.17 0.18 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.613 (+/-0.240) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.664 (+/-0.318) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.3334580622810015e-05}
0.638 (+/-0.324) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.3768402866248774e-05}
0.662 (+/-0.316) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.4210290467361783e-05}
0.638 (+/-0.238) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.466039337234341e-05}
0.688 (+/-0.298) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.638 (+/-0.237) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5585858869056458e-05}
0.688 (+/-0.298) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.606153549998898e-05}
0.638 (+/-0.327) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.654605561975541e-05}
0.664 (+/-0.224) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.7039583641088475e-05}
0.638 (+/-0.237) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.613 (+/-0.240) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.639 (+/-0.238) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 2.3334580622810015e-05}
0.638 (+/-0.324) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 2.3768402866248774e-05}
0.663 (+/-0.317) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 2.4210290467361783e-05}
0.638 (+/-0.239) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 2.466039337234341e-05}
0.688 (+/-0.297) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.638 (+/-0.237) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 2.5585858869056458e-05}
0.688 (+/-0.298) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 2.606153549998898e-05}
0.638 (+/-0.327) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 2.654605561975541e-05}
0.664 (+/-0.225) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 2.7039583641088475e-05}
0.638 (+/-0.237) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.613 (+/-0.241) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.639 (+/-0.238) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 2.3334580622810015e-05}
0.638 (+/-0.323) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 2.3768402866248774e-05}
0.663 (+/-0.316) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 2.4210290467361783e-05}
0.638 (+/-0.239) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 2.466039337234341e-05}
0.688 (+/-0.298) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.638 (+/-0.237) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 2.5585858869056458e-05}
0.688 (+/-0.298) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 2.606153549998898e-05}
0.663 (+/-0.317) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 2.654605561975541e-05}
0.664 (+/-0.225) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 2.7039583641088475e-05}
0.638 (+/-0.237) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.613 (+/-0.241) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.639 (+/-0.239) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 2.3334580622810015e-05}
0.637 (+/-0.324) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 2.3768402866248774e-05}
0.663 (+/-0.316) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 2.4210290467361783e-05}
0.638 (+/-0.239) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 2.466039337234341e-05}
0.688 (+/-0.298) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.638 (+/-0.237) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 2.5585858869056458e-05}
0.663 (+/-0.315) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 2.606153549998898e-05}
0.637 (+/-0.238) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 2.654605561975541e-05}
0.664 (+/-0.225) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 2.7039583641088475e-05}
0.639 (+/-0.236) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.613 (+/-0.241) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.639 (+/-0.239) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 2.3334580622810015e-05}
0.637 (+/-0.324) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 2.3768402866248774e-05}
0.663 (+/-0.316) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 2.4210290467361783e-05}
0.638 (+/-0.239) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 2.466039337234341e-05}
0.689 (+/-0.298) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.638 (+/-0.237) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 2.5585858869056458e-05}
0.664 (+/-0.316) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 2.606153549998898e-05}
0.638 (+/-0.238) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 2.654605561975541e-05}
0.639 (+/-0.238) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 2.7039583641088475e-05}
0.639 (+/-0.236) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.613 (+/-0.241) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.639 (+/-0.239) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.3334580622810015e-05}
0.663 (+/-0.315) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.3768402866248774e-05}
0.688 (+/-0.299) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.4210290467361783e-05}
0.638 (+/-0.239) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.466039337234341e-05}
0.689 (+/-0.298) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.637 (+/-0.237) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.5585858869056458e-05}
0.664 (+/-0.316) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.606153549998898e-05}
0.638 (+/-0.238) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.654605561975541e-05}
0.664 (+/-0.225) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.7039583641088475e-05}
0.664 (+/-0.224) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.613 (+/-0.240) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.639 (+/-0.239) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 2.3334580622810015e-05}
0.663 (+/-0.315) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 2.3768402866248774e-05}
0.688 (+/-0.299) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 2.4210290467361783e-05}
0.638 (+/-0.238) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 2.466039337234341e-05}
0.688 (+/-0.299) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.637 (+/-0.236) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 2.5585858869056458e-05}
0.664 (+/-0.316) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 2.606153549998898e-05}
0.638 (+/-0.238) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 2.654605561975541e-05}
0.664 (+/-0.225) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 2.7039583641088475e-05}
0.664 (+/-0.224) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.613 (+/-0.241) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.639 (+/-0.239) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 2.3334580622810015e-05}
0.662 (+/-0.315) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 2.3768402866248774e-05}
0.688 (+/-0.299) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 2.4210290467361783e-05}
0.638 (+/-0.238) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 2.466039337234341e-05}
0.688 (+/-0.299) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.637 (+/-0.236) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 2.5585858869056458e-05}
0.664 (+/-0.316) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 2.606153549998898e-05}
0.638 (+/-0.238) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 2.654605561975541e-05}
0.664 (+/-0.225) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 2.7039583641088475e-05}
0.664 (+/-0.224) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.613 (+/-0.241) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.639 (+/-0.238) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 2.3334580622810015e-05}
0.663 (+/-0.315) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 2.3768402866248774e-05}
0.663 (+/-0.316) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 2.4210290467361783e-05}
0.638 (+/-0.238) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 2.466039337234341e-05}
0.688 (+/-0.299) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.637 (+/-0.236) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 2.5585858869056458e-05}
0.664 (+/-0.315) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 2.606153549998898e-05}
0.638 (+/-0.238) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 2.654605561975541e-05}
0.665 (+/-0.225) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 2.7039583641088475e-05}
0.664 (+/-0.224) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.613 (+/-0.241) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.638 (+/-0.239) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.3334580622810015e-05}
0.663 (+/-0.315) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.3768402866248774e-05}
0.663 (+/-0.316) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.4210290467361783e-05}
0.638 (+/-0.237) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.466039337234341e-05}
0.688 (+/-0.300) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.638 (+/-0.235) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.5585858869056458e-05}
0.663 (+/-0.315) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.606153549998898e-05}
0.637 (+/-0.239) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.654605561975541e-05}
0.664 (+/-0.225) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.7039583641088475e-05}
0.689 (+/-0.300) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.613 (+/-0.241) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.290867652767773e-05}
0.638 (+/-0.239) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.3334580622810015e-05}
0.638 (+/-0.324) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.3768402866248774e-05}
0.662 (+/-0.316) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.4210290467361783e-05}
0.638 (+/-0.237) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.466039337234341e-05}
0.688 (+/-0.300) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.637 (+/-0.236) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.5585858869056458e-05}
0.663 (+/-0.315) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.606153549998898e-05}
0.637 (+/-0.239) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.654605561975541e-05}
0.639 (+/-0.238) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.7039583641088475e-05}
0.664 (+/-0.317) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.663 (+/-0.316) for {'C': 10.0, 'kernel': 'linear'}
0.662 (+/-0.315) for {'C': 100.0, 'kernel': 'linear'}
0.613 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
0.588 (+/-0.233) for {'C': 10000.0, 'kernel': 'linear'}
0.588 (+/-0.233) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.10 0.17 0.12 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.688941792398384
发现最优参数gamma为原先的最大/最小值,直接重新设置超参。
第1轮loop中,tunemodel函数得到的最优参数是: ([{'C': array([20892961.30854038, 21281390.4598271 , 21677041.04819691,
22080047.33018901, 22490546.05835781, 22908676.52767773,
23334580.62281001, 23768402.86624874, 24210290.46736181,
24660393.37234341, 25118864.31509581]), 'kernel': ['rbf'], 'gamma': array([2.7542287e-10, 2.7542287e-09, 2.7542287e-08, 2.7542287e-07,
2.7542287e-06, 2.7542287e-05, 2.7542287e-04, 2.7542287e-03,
2.7542287e-02, 2.7542287e-01, 2.7542287e+00])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}], {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.7542287033381694e-05}, 0.688941792398384)
这是第1次迭代微调C和gamma。
第1次迭代,得到delta: [7.05974460e+06 2.42342272e-06]
执行tunemodel()函数前,使用的grid search parameter是:
[{'C': array([20892961.30854038, 21281390.4598271 , 21677041.04819691,
22080047.33018901, 22490546.05835781, 22908676.52767773,
23334580.62281001, 23768402.86624874, 24210290.46736181,
24660393.37234341, 25118864.31509581]), 'kernel': ['rbf'], 'gamma': array([2.7542287e-10, 2.7542287e-09, 2.7542287e-08, 2.7542287e-07,
2.7542287e-06, 2.7542287e-05, 2.7542287e-04, 2.7542287e-03,
2.7542287e-02, 2.7542287e-01, 2.7542287e+00])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.496 (+/-0.001) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-10}
0.747 (+/-0.502) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-09}
0.547 (+/-0.300) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-08}
0.678 (+/-0.431) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 2.754228703338175e-07}
0.635 (+/-0.307) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 2.7542287033381744e-06}
0.664 (+/-0.299) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-05}
0.634 (+/-0.308) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 0.00027542287033381743}
0.614 (+/-0.327) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 0.0027542287033381747}
0.614 (+/-0.327) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 0.027542287033381744}
0.614 (+/-0.308) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 0.27542287033381746}
0.672 (+/-0.451) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 2.754228703338169}
0.496 (+/-0.001) for {'C': 21281390.459827095, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-10}
0.747 (+/-0.502) for {'C': 21281390.459827095, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-09}
0.547 (+/-0.300) for {'C': 21281390.459827095, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-08}
0.678 (+/-0.431) for {'C': 21281390.459827095, 'kernel': 'rbf', 'gamma': 2.754228703338175e-07}
0.635 (+/-0.307) for {'C': 21281390.459827095, 'kernel': 'rbf', 'gamma': 2.7542287033381744e-06}
0.666 (+/-0.297) for {'C': 21281390.459827095, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-05}
0.634 (+/-0.308) for {'C': 21281390.459827095, 'kernel': 'rbf', 'gamma': 0.00027542287033381743}
0.614 (+/-0.327) for {'C': 21281390.459827095, 'kernel': 'rbf', 'gamma': 0.0027542287033381747}
0.614 (+/-0.327) for {'C': 21281390.459827095, 'kernel': 'rbf', 'gamma': 0.027542287033381744}
0.614 (+/-0.308) for {'C': 21281390.459827095, 'kernel': 'rbf', 'gamma': 0.27542287033381746}
0.672 (+/-0.451) for {'C': 21281390.459827095, 'kernel': 'rbf', 'gamma': 2.754228703338169}
0.496 (+/-0.001) for {'C': 21677041.04819691, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-10}
0.747 (+/-0.502) for {'C': 21677041.04819691, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-09}
0.547 (+/-0.300) for {'C': 21677041.04819691, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-08}
0.678 (+/-0.431) for {'C': 21677041.04819691, 'kernel': 'rbf', 'gamma': 2.754228703338175e-07}
0.635 (+/-0.307) for {'C': 21677041.04819691, 'kernel': 'rbf', 'gamma': 2.7542287033381744e-06}
0.666 (+/-0.297) for {'C': 21677041.04819691, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-05}
0.634 (+/-0.308) for {'C': 21677041.04819691, 'kernel': 'rbf', 'gamma': 0.00027542287033381743}
0.614 (+/-0.327) for {'C': 21677041.04819691, 'kernel': 'rbf', 'gamma': 0.0027542287033381747}
0.614 (+/-0.327) for {'C': 21677041.04819691, 'kernel': 'rbf', 'gamma': 0.027542287033381744}
0.614 (+/-0.308) for {'C': 21677041.04819691, 'kernel': 'rbf', 'gamma': 0.27542287033381746}
0.672 (+/-0.451) for {'C': 21677041.04819691, 'kernel': 'rbf', 'gamma': 2.754228703338169}
0.496 (+/-0.001) for {'C': 22080047.33018901, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-10}
0.747 (+/-0.502) for {'C': 22080047.33018901, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-09}
0.547 (+/-0.300) for {'C': 22080047.33018901, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-08}
0.678 (+/-0.431) for {'C': 22080047.33018901, 'kernel': 'rbf', 'gamma': 2.754228703338175e-07}
0.635 (+/-0.307) for {'C': 22080047.33018901, 'kernel': 'rbf', 'gamma': 2.7542287033381744e-06}
0.674 (+/-0.311) for {'C': 22080047.33018901, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-05}
0.634 (+/-0.308) for {'C': 22080047.33018901, 'kernel': 'rbf', 'gamma': 0.00027542287033381743}
0.614 (+/-0.327) for {'C': 22080047.33018901, 'kernel': 'rbf', 'gamma': 0.0027542287033381747}
0.614 (+/-0.327) for {'C': 22080047.33018901, 'kernel': 'rbf', 'gamma': 0.027542287033381744}
0.614 (+/-0.308) for {'C': 22080047.33018901, 'kernel': 'rbf', 'gamma': 0.27542287033381746}
0.672 (+/-0.451) for {'C': 22080047.33018901, 'kernel': 'rbf', 'gamma': 2.754228703338169}
0.496 (+/-0.001) for {'C': 22490546.058357812, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-10}
0.747 (+/-0.502) for {'C': 22490546.058357812, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-09}
0.548 (+/-0.299) for {'C': 22490546.058357812, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-08}
0.678 (+/-0.431) for {'C': 22490546.058357812, 'kernel': 'rbf', 'gamma': 2.754228703338175e-07}
0.635 (+/-0.307) for {'C': 22490546.058357812, 'kernel': 'rbf', 'gamma': 2.7542287033381744e-06}
0.683 (+/-0.314) for {'C': 22490546.058357812, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-05}
0.634 (+/-0.308) for {'C': 22490546.058357812, 'kernel': 'rbf', 'gamma': 0.00027542287033381743}
0.614 (+/-0.327) for {'C': 22490546.058357812, 'kernel': 'rbf', 'gamma': 0.0027542287033381747}
0.614 (+/-0.327) for {'C': 22490546.058357812, 'kernel': 'rbf', 'gamma': 0.027542287033381744}
0.614 (+/-0.308) for {'C': 22490546.058357812, 'kernel': 'rbf', 'gamma': 0.27542287033381746}
0.672 (+/-0.451) for {'C': 22490546.058357812, 'kernel': 'rbf', 'gamma': 2.754228703338169}
0.496 (+/-0.001) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-10}
0.747 (+/-0.502) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-09}
0.548 (+/-0.299) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-08}
0.672 (+/-0.439) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.754228703338175e-07}
0.635 (+/-0.307) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.7542287033381744e-06}
0.683 (+/-0.314) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-05}
0.634 (+/-0.308) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 0.00027542287033381743}
0.614 (+/-0.327) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 0.0027542287033381747}
0.614 (+/-0.327) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 0.027542287033381744}
0.614 (+/-0.308) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 0.27542287033381746}
0.672 (+/-0.451) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.754228703338169}
0.496 (+/-0.001) for {'C': 23334580.622810014, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-10}
0.747 (+/-0.502) for {'C': 23334580.622810014, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-09}
0.548 (+/-0.299) for {'C': 23334580.622810014, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-08}
0.678 (+/-0.431) for {'C': 23334580.622810014, 'kernel': 'rbf', 'gamma': 2.754228703338175e-07}
0.635 (+/-0.307) for {'C': 23334580.622810014, 'kernel': 'rbf', 'gamma': 2.7542287033381744e-06}
0.683 (+/-0.314) for {'C': 23334580.622810014, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-05}
0.634 (+/-0.308) for {'C': 23334580.622810014, 'kernel': 'rbf', 'gamma': 0.00027542287033381743}
0.614 (+/-0.327) for {'C': 23334580.622810014, 'kernel': 'rbf', 'gamma': 0.0027542287033381747}
0.614 (+/-0.327) for {'C': 23334580.622810014, 'kernel': 'rbf', 'gamma': 0.027542287033381744}
0.614 (+/-0.308) for {'C': 23334580.622810014, 'kernel': 'rbf', 'gamma': 0.27542287033381746}
0.672 (+/-0.451) for {'C': 23334580.622810014, 'kernel': 'rbf', 'gamma': 2.754228703338169}
0.496 (+/-0.001) for {'C': 23768402.86624874, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-10}
0.747 (+/-0.502) for {'C': 23768402.86624874, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-09}
0.548 (+/-0.299) for {'C': 23768402.86624874, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-08}
0.678 (+/-0.431) for {'C': 23768402.86624874, 'kernel': 'rbf', 'gamma': 2.754228703338175e-07}
0.635 (+/-0.307) for {'C': 23768402.86624874, 'kernel': 'rbf', 'gamma': 2.7542287033381744e-06}
0.658 (+/-0.329) for {'C': 23768402.86624874, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-05}
0.634 (+/-0.308) for {'C': 23768402.86624874, 'kernel': 'rbf', 'gamma': 0.00027542287033381743}
0.614 (+/-0.327) for {'C': 23768402.86624874, 'kernel': 'rbf', 'gamma': 0.0027542287033381747}
0.614 (+/-0.327) for {'C': 23768402.86624874, 'kernel': 'rbf', 'gamma': 0.027542287033381744}
0.614 (+/-0.308) for {'C': 23768402.86624874, 'kernel': 'rbf', 'gamma': 0.27542287033381746}
0.672 (+/-0.451) for {'C': 23768402.86624874, 'kernel': 'rbf', 'gamma': 2.754228703338169}
0.496 (+/-0.001) for {'C': 24210290.467361808, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-10}
0.747 (+/-0.502) for {'C': 24210290.467361808, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-09}
0.548 (+/-0.299) for {'C': 24210290.467361808, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-08}
0.677 (+/-0.432) for {'C': 24210290.467361808, 'kernel': 'rbf', 'gamma': 2.754228703338175e-07}
0.635 (+/-0.307) for {'C': 24210290.467361808, 'kernel': 'rbf', 'gamma': 2.7542287033381744e-06}
0.660 (+/-0.327) for {'C': 24210290.467361808, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-05}
0.634 (+/-0.308) for {'C': 24210290.467361808, 'kernel': 'rbf', 'gamma': 0.00027542287033381743}
0.614 (+/-0.327) for {'C': 24210290.467361808, 'kernel': 'rbf', 'gamma': 0.0027542287033381747}
0.614 (+/-0.327) for {'C': 24210290.467361808, 'kernel': 'rbf', 'gamma': 0.027542287033381744}
0.614 (+/-0.308) for {'C': 24210290.467361808, 'kernel': 'rbf', 'gamma': 0.27542287033381746}
0.672 (+/-0.451) for {'C': 24210290.467361808, 'kernel': 'rbf', 'gamma': 2.754228703338169}
0.496 (+/-0.001) for {'C': 24660393.37234341, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-10}
0.747 (+/-0.502) for {'C': 24660393.37234341, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-09}
0.548 (+/-0.299) for {'C': 24660393.37234341, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-08}
0.677 (+/-0.432) for {'C': 24660393.37234341, 'kernel': 'rbf', 'gamma': 2.754228703338175e-07}
0.635 (+/-0.307) for {'C': 24660393.37234341, 'kernel': 'rbf', 'gamma': 2.7542287033381744e-06}
0.660 (+/-0.327) for {'C': 24660393.37234341, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-05}
0.634 (+/-0.308) for {'C': 24660393.37234341, 'kernel': 'rbf', 'gamma': 0.00027542287033381743}
0.614 (+/-0.327) for {'C': 24660393.37234341, 'kernel': 'rbf', 'gamma': 0.0027542287033381747}
0.614 (+/-0.327) for {'C': 24660393.37234341, 'kernel': 'rbf', 'gamma': 0.027542287033381744}
0.614 (+/-0.308) for {'C': 24660393.37234341, 'kernel': 'rbf', 'gamma': 0.27542287033381746}
0.672 (+/-0.451) for {'C': 24660393.37234341, 'kernel': 'rbf', 'gamma': 2.754228703338169}
0.496 (+/-0.001) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-10}
0.747 (+/-0.502) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-09}
0.548 (+/-0.299) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-08}
0.677 (+/-0.432) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.754228703338175e-07}
0.635 (+/-0.307) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.7542287033381744e-06}
0.660 (+/-0.327) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-05}
0.634 (+/-0.308) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.00027542287033381743}
0.614 (+/-0.327) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.0027542287033381747}
0.614 (+/-0.327) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.027542287033381744}
0.614 (+/-0.308) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.27542287033381746}
0.672 (+/-0.451) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.754228703338169}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'linear'}
0.706 (+/-0.383) for {'C': 10.0, 'kernel': 'linear'}
0.641 (+/-0.312) for {'C': 100.0, 'kernel': 'linear'}
0.622 (+/-0.319) for {'C': 1000.0, 'kernel': 'linear'}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'linear'}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [1]
检查交叉验证集中测试集标签是否有预测不到的值: {-1}
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 1.00 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.500 (+/-0.000) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-10}
0.617 (+/-0.238) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-09}
0.510 (+/-0.251) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-08}
0.656 (+/-0.309) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 2.754228703338175e-07}
0.662 (+/-0.314) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 2.7542287033381744e-06}
0.664 (+/-0.224) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-05}
0.638 (+/-0.237) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 0.00027542287033381743}
0.588 (+/-0.233) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 0.0027542287033381747}
0.588 (+/-0.232) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 0.027542287033381744}
0.614 (+/-0.237) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 0.27542287033381746}
0.590 (+/-0.231) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 2.754228703338169}
0.500 (+/-0.000) for {'C': 21281390.459827095, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-10}
0.617 (+/-0.238) for {'C': 21281390.459827095, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-09}
0.510 (+/-0.252) for {'C': 21281390.459827095, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-08}
0.656 (+/-0.309) for {'C': 21281390.459827095, 'kernel': 'rbf', 'gamma': 2.754228703338175e-07}
0.662 (+/-0.314) for {'C': 21281390.459827095, 'kernel': 'rbf', 'gamma': 2.7542287033381744e-06}
0.664 (+/-0.224) for {'C': 21281390.459827095, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-05}
0.638 (+/-0.237) for {'C': 21281390.459827095, 'kernel': 'rbf', 'gamma': 0.00027542287033381743}
0.588 (+/-0.233) for {'C': 21281390.459827095, 'kernel': 'rbf', 'gamma': 0.0027542287033381747}
0.588 (+/-0.232) for {'C': 21281390.459827095, 'kernel': 'rbf', 'gamma': 0.027542287033381744}
0.614 (+/-0.237) for {'C': 21281390.459827095, 'kernel': 'rbf', 'gamma': 0.27542287033381746}
0.590 (+/-0.231) for {'C': 21281390.459827095, 'kernel': 'rbf', 'gamma': 2.754228703338169}
0.500 (+/-0.000) for {'C': 21677041.04819691, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-10}
0.617 (+/-0.238) for {'C': 21677041.04819691, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-09}
0.509 (+/-0.253) for {'C': 21677041.04819691, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-08}
0.656 (+/-0.309) for {'C': 21677041.04819691, 'kernel': 'rbf', 'gamma': 2.754228703338175e-07}
0.662 (+/-0.314) for {'C': 21677041.04819691, 'kernel': 'rbf', 'gamma': 2.7542287033381744e-06}
0.664 (+/-0.224) for {'C': 21677041.04819691, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-05}
0.638 (+/-0.237) for {'C': 21677041.04819691, 'kernel': 'rbf', 'gamma': 0.00027542287033381743}
0.588 (+/-0.233) for {'C': 21677041.04819691, 'kernel': 'rbf', 'gamma': 0.0027542287033381747}
0.588 (+/-0.232) for {'C': 21677041.04819691, 'kernel': 'rbf', 'gamma': 0.027542287033381744}
0.614 (+/-0.237) for {'C': 21677041.04819691, 'kernel': 'rbf', 'gamma': 0.27542287033381746}
0.590 (+/-0.231) for {'C': 21677041.04819691, 'kernel': 'rbf', 'gamma': 2.754228703338169}
0.500 (+/-0.000) for {'C': 22080047.33018901, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-10}
0.617 (+/-0.238) for {'C': 22080047.33018901, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-09}
0.509 (+/-0.253) for {'C': 22080047.33018901, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-08}
0.656 (+/-0.309) for {'C': 22080047.33018901, 'kernel': 'rbf', 'gamma': 2.754228703338175e-07}
0.662 (+/-0.314) for {'C': 22080047.33018901, 'kernel': 'rbf', 'gamma': 2.7542287033381744e-06}
0.689 (+/-0.300) for {'C': 22080047.33018901, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-05}
0.638 (+/-0.237) for {'C': 22080047.33018901, 'kernel': 'rbf', 'gamma': 0.00027542287033381743}
0.588 (+/-0.233) for {'C': 22080047.33018901, 'kernel': 'rbf', 'gamma': 0.0027542287033381747}
0.588 (+/-0.232) for {'C': 22080047.33018901, 'kernel': 'rbf', 'gamma': 0.027542287033381744}
0.614 (+/-0.237) for {'C': 22080047.33018901, 'kernel': 'rbf', 'gamma': 0.27542287033381746}
0.590 (+/-0.231) for {'C': 22080047.33018901, 'kernel': 'rbf', 'gamma': 2.754228703338169}
0.500 (+/-0.000) for {'C': 22490546.058357812, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-10}
0.617 (+/-0.238) for {'C': 22490546.058357812, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-09}
0.533 (+/-0.208) for {'C': 22490546.058357812, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-08}
0.656 (+/-0.309) for {'C': 22490546.058357812, 'kernel': 'rbf', 'gamma': 2.754228703338175e-07}
0.662 (+/-0.314) for {'C': 22490546.058357812, 'kernel': 'rbf', 'gamma': 2.7542287033381744e-06}
0.689 (+/-0.300) for {'C': 22490546.058357812, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-05}
0.638 (+/-0.237) for {'C': 22490546.058357812, 'kernel': 'rbf', 'gamma': 0.00027542287033381743}
0.588 (+/-0.233) for {'C': 22490546.058357812, 'kernel': 'rbf', 'gamma': 0.0027542287033381747}
0.588 (+/-0.232) for {'C': 22490546.058357812, 'kernel': 'rbf', 'gamma': 0.027542287033381744}
0.614 (+/-0.237) for {'C': 22490546.058357812, 'kernel': 'rbf', 'gamma': 0.27542287033381746}
0.590 (+/-0.231) for {'C': 22490546.058357812, 'kernel': 'rbf', 'gamma': 2.754228703338169}
0.500 (+/-0.000) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-10}
0.617 (+/-0.238) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-09}
0.533 (+/-0.208) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-08}
0.631 (+/-0.319) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.754228703338175e-07}
0.662 (+/-0.315) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.7542287033381744e-06}
0.689 (+/-0.300) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-05}
0.638 (+/-0.237) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 0.00027542287033381743}
0.588 (+/-0.233) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 0.0027542287033381747}
0.588 (+/-0.232) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 0.027542287033381744}
0.614 (+/-0.237) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 0.27542287033381746}
0.590 (+/-0.231) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.754228703338169}
0.500 (+/-0.000) for {'C': 23334580.622810014, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-10}
0.617 (+/-0.238) for {'C': 23334580.622810014, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-09}
0.533 (+/-0.208) for {'C': 23334580.622810014, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-08}
0.656 (+/-0.309) for {'C': 23334580.622810014, 'kernel': 'rbf', 'gamma': 2.754228703338175e-07}
0.662 (+/-0.315) for {'C': 23334580.622810014, 'kernel': 'rbf', 'gamma': 2.7542287033381744e-06}
0.689 (+/-0.300) for {'C': 23334580.622810014, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-05}
0.638 (+/-0.237) for {'C': 23334580.622810014, 'kernel': 'rbf', 'gamma': 0.00027542287033381743}
0.588 (+/-0.233) for {'C': 23334580.622810014, 'kernel': 'rbf', 'gamma': 0.0027542287033381747}
0.588 (+/-0.232) for {'C': 23334580.622810014, 'kernel': 'rbf', 'gamma': 0.027542287033381744}
0.614 (+/-0.237) for {'C': 23334580.622810014, 'kernel': 'rbf', 'gamma': 0.27542287033381746}
0.590 (+/-0.231) for {'C': 23334580.622810014, 'kernel': 'rbf', 'gamma': 2.754228703338169}
0.500 (+/-0.000) for {'C': 23768402.86624874, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-10}
0.617 (+/-0.238) for {'C': 23768402.86624874, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-09}
0.532 (+/-0.208) for {'C': 23768402.86624874, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-08}
0.656 (+/-0.309) for {'C': 23768402.86624874, 'kernel': 'rbf', 'gamma': 2.754228703338175e-07}
0.662 (+/-0.315) for {'C': 23768402.86624874, 'kernel': 'rbf', 'gamma': 2.7542287033381744e-06}
0.664 (+/-0.317) for {'C': 23768402.86624874, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-05}
0.638 (+/-0.237) for {'C': 23768402.86624874, 'kernel': 'rbf', 'gamma': 0.00027542287033381743}
0.588 (+/-0.233) for {'C': 23768402.86624874, 'kernel': 'rbf', 'gamma': 0.0027542287033381747}
0.588 (+/-0.232) for {'C': 23768402.86624874, 'kernel': 'rbf', 'gamma': 0.027542287033381744}
0.614 (+/-0.237) for {'C': 23768402.86624874, 'kernel': 'rbf', 'gamma': 0.27542287033381746}
0.590 (+/-0.231) for {'C': 23768402.86624874, 'kernel': 'rbf', 'gamma': 2.754228703338169}
0.500 (+/-0.000) for {'C': 24210290.467361808, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-10}
0.617 (+/-0.238) for {'C': 24210290.467361808, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-09}
0.532 (+/-0.208) for {'C': 24210290.467361808, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-08}
0.656 (+/-0.309) for {'C': 24210290.467361808, 'kernel': 'rbf', 'gamma': 2.754228703338175e-07}
0.662 (+/-0.315) for {'C': 24210290.467361808, 'kernel': 'rbf', 'gamma': 2.7542287033381744e-06}
0.664 (+/-0.317) for {'C': 24210290.467361808, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-05}
0.638 (+/-0.237) for {'C': 24210290.467361808, 'kernel': 'rbf', 'gamma': 0.00027542287033381743}
0.588 (+/-0.233) for {'C': 24210290.467361808, 'kernel': 'rbf', 'gamma': 0.0027542287033381747}
0.588 (+/-0.232) for {'C': 24210290.467361808, 'kernel': 'rbf', 'gamma': 0.027542287033381744}
0.614 (+/-0.237) for {'C': 24210290.467361808, 'kernel': 'rbf', 'gamma': 0.27542287033381746}
0.590 (+/-0.231) for {'C': 24210290.467361808, 'kernel': 'rbf', 'gamma': 2.754228703338169}
0.500 (+/-0.000) for {'C': 24660393.37234341, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-10}
0.617 (+/-0.238) for {'C': 24660393.37234341, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-09}
0.531 (+/-0.207) for {'C': 24660393.37234341, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-08}
0.656 (+/-0.309) for {'C': 24660393.37234341, 'kernel': 'rbf', 'gamma': 2.754228703338175e-07}
0.662 (+/-0.315) for {'C': 24660393.37234341, 'kernel': 'rbf', 'gamma': 2.7542287033381744e-06}
0.664 (+/-0.317) for {'C': 24660393.37234341, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-05}
0.638 (+/-0.237) for {'C': 24660393.37234341, 'kernel': 'rbf', 'gamma': 0.00027542287033381743}
0.588 (+/-0.233) for {'C': 24660393.37234341, 'kernel': 'rbf', 'gamma': 0.0027542287033381747}
0.588 (+/-0.232) for {'C': 24660393.37234341, 'kernel': 'rbf', 'gamma': 0.027542287033381744}
0.614 (+/-0.237) for {'C': 24660393.37234341, 'kernel': 'rbf', 'gamma': 0.27542287033381746}
0.590 (+/-0.231) for {'C': 24660393.37234341, 'kernel': 'rbf', 'gamma': 2.754228703338169}
0.500 (+/-0.000) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-10}
0.617 (+/-0.238) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-09}
0.531 (+/-0.208) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-08}
0.656 (+/-0.309) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.754228703338175e-07}
0.662 (+/-0.315) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.7542287033381744e-06}
0.664 (+/-0.317) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-05}
0.638 (+/-0.237) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.00027542287033381743}
0.588 (+/-0.233) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.0027542287033381747}
0.588 (+/-0.232) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.027542287033381744}
0.614 (+/-0.237) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.27542287033381746}
0.590 (+/-0.231) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.754228703338169}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'linear'}
0.666 (+/-0.315) for {'C': 10.0, 'kernel': 'linear'}
0.663 (+/-0.316) for {'C': 100.0, 'kernel': 'linear'}
0.613 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
0.588 (+/-0.233) for {'C': 10000.0, 'kernel': 'linear'}
0.588 (+/-0.233) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.10 0.17 0.12 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 22490546.058357812, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-05}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.688941792398384
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.496 (+/-0.001) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-10}
0.747 (+/-0.502) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-09}
0.548 (+/-0.300) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-08}
0.599 (+/-0.302) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 2.754228703338175e-07}
0.621 (+/-0.318) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 2.7542287033381744e-06}
0.664 (+/-0.299) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-05}
0.634 (+/-0.308) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 0.00027542287033381743}
0.614 (+/-0.327) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 0.0027542287033381747}
0.614 (+/-0.327) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 0.027542287033381744}
0.614 (+/-0.308) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 0.27542287033381746}
0.672 (+/-0.451) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 2.754228703338169}
0.496 (+/-0.001) for {'C': 21281390.459827095, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-10}
0.747 (+/-0.502) for {'C': 21281390.459827095, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-09}
0.547 (+/-0.300) for {'C': 21281390.459827095, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-08}
0.623 (+/-0.379) for {'C': 21281390.459827095, 'kernel': 'rbf', 'gamma': 2.754228703338175e-07}
0.621 (+/-0.318) for {'C': 21281390.459827095, 'kernel': 'rbf', 'gamma': 2.7542287033381744e-06}
0.666 (+/-0.297) for {'C': 21281390.459827095, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-05}
0.634 (+/-0.308) for {'C': 21281390.459827095, 'kernel': 'rbf', 'gamma': 0.00027542287033381743}
0.614 (+/-0.327) for {'C': 21281390.459827095, 'kernel': 'rbf', 'gamma': 0.0027542287033381747}
0.614 (+/-0.327) for {'C': 21281390.459827095, 'kernel': 'rbf', 'gamma': 0.027542287033381744}
0.614 (+/-0.308) for {'C': 21281390.459827095, 'kernel': 'rbf', 'gamma': 0.27542287033381746}
0.672 (+/-0.451) for {'C': 21281390.459827095, 'kernel': 'rbf', 'gamma': 2.754228703338169}
0.496 (+/-0.001) for {'C': 21677041.04819691, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-10}
0.747 (+/-0.502) for {'C': 21677041.04819691, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-09}
0.547 (+/-0.300) for {'C': 21677041.04819691, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-08}
0.599 (+/-0.302) for {'C': 21677041.04819691, 'kernel': 'rbf', 'gamma': 2.754228703338175e-07}
0.621 (+/-0.318) for {'C': 21677041.04819691, 'kernel': 'rbf', 'gamma': 2.7542287033381744e-06}
0.666 (+/-0.297) for {'C': 21677041.04819691, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-05}
0.634 (+/-0.308) for {'C': 21677041.04819691, 'kernel': 'rbf', 'gamma': 0.00027542287033381743}
0.614 (+/-0.327) for {'C': 21677041.04819691, 'kernel': 'rbf', 'gamma': 0.0027542287033381747}
0.614 (+/-0.327) for {'C': 21677041.04819691, 'kernel': 'rbf', 'gamma': 0.027542287033381744}
0.614 (+/-0.308) for {'C': 21677041.04819691, 'kernel': 'rbf', 'gamma': 0.27542287033381746}
0.672 (+/-0.451) for {'C': 21677041.04819691, 'kernel': 'rbf', 'gamma': 2.754228703338169}
0.496 (+/-0.001) for {'C': 22080047.33018901, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-10}
0.747 (+/-0.502) for {'C': 22080047.33018901, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-09}
0.547 (+/-0.300) for {'C': 22080047.33018901, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-08}
0.624 (+/-0.379) for {'C': 22080047.33018901, 'kernel': 'rbf', 'gamma': 2.754228703338175e-07}
0.621 (+/-0.318) for {'C': 22080047.33018901, 'kernel': 'rbf', 'gamma': 2.7542287033381744e-06}
0.674 (+/-0.311) for {'C': 22080047.33018901, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-05}
0.634 (+/-0.308) for {'C': 22080047.33018901, 'kernel': 'rbf', 'gamma': 0.00027542287033381743}
0.614 (+/-0.327) for {'C': 22080047.33018901, 'kernel': 'rbf', 'gamma': 0.0027542287033381747}
0.614 (+/-0.327) for {'C': 22080047.33018901, 'kernel': 'rbf', 'gamma': 0.027542287033381744}
0.614 (+/-0.308) for {'C': 22080047.33018901, 'kernel': 'rbf', 'gamma': 0.27542287033381746}
0.672 (+/-0.451) for {'C': 22080047.33018901, 'kernel': 'rbf', 'gamma': 2.754228703338169}
0.496 (+/-0.001) for {'C': 22490546.058357812, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-10}
0.747 (+/-0.502) for {'C': 22490546.058357812, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-09}
0.547 (+/-0.300) for {'C': 22490546.058357812, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-08}
0.598 (+/-0.302) for {'C': 22490546.058357812, 'kernel': 'rbf', 'gamma': 2.754228703338175e-07}
0.621 (+/-0.318) for {'C': 22490546.058357812, 'kernel': 'rbf', 'gamma': 2.7542287033381744e-06}
0.683 (+/-0.314) for {'C': 22490546.058357812, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-05}
0.634 (+/-0.308) for {'C': 22490546.058357812, 'kernel': 'rbf', 'gamma': 0.00027542287033381743}
0.614 (+/-0.327) for {'C': 22490546.058357812, 'kernel': 'rbf', 'gamma': 0.0027542287033381747}
0.614 (+/-0.327) for {'C': 22490546.058357812, 'kernel': 'rbf', 'gamma': 0.027542287033381744}
0.614 (+/-0.308) for {'C': 22490546.058357812, 'kernel': 'rbf', 'gamma': 0.27542287033381746}
0.672 (+/-0.451) for {'C': 22490546.058357812, 'kernel': 'rbf', 'gamma': 2.754228703338169}
0.496 (+/-0.001) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-10}
0.747 (+/-0.502) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-09}
0.548 (+/-0.299) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-08}
0.623 (+/-0.379) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.754228703338175e-07}
0.621 (+/-0.318) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.7542287033381744e-06}
0.683 (+/-0.314) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-05}
0.634 (+/-0.308) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 0.00027542287033381743}
0.614 (+/-0.327) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 0.0027542287033381747}
0.614 (+/-0.327) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 0.027542287033381744}
0.614 (+/-0.308) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 0.27542287033381746}
0.672 (+/-0.451) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.754228703338169}
0.496 (+/-0.001) for {'C': 23334580.622810014, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-10}
0.747 (+/-0.502) for {'C': 23334580.622810014, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-09}
0.547 (+/-0.300) for {'C': 23334580.622810014, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-08}
0.600 (+/-0.302) for {'C': 23334580.622810014, 'kernel': 'rbf', 'gamma': 2.754228703338175e-07}
0.621 (+/-0.318) for {'C': 23334580.622810014, 'kernel': 'rbf', 'gamma': 2.7542287033381744e-06}
0.683 (+/-0.314) for {'C': 23334580.622810014, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-05}
0.634 (+/-0.308) for {'C': 23334580.622810014, 'kernel': 'rbf', 'gamma': 0.00027542287033381743}
0.614 (+/-0.327) for {'C': 23334580.622810014, 'kernel': 'rbf', 'gamma': 0.0027542287033381747}
0.614 (+/-0.327) for {'C': 23334580.622810014, 'kernel': 'rbf', 'gamma': 0.027542287033381744}
0.614 (+/-0.308) for {'C': 23334580.622810014, 'kernel': 'rbf', 'gamma': 0.27542287033381746}
0.672 (+/-0.451) for {'C': 23334580.622810014, 'kernel': 'rbf', 'gamma': 2.754228703338169}
0.496 (+/-0.001) for {'C': 23768402.86624874, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-10}
0.747 (+/-0.502) for {'C': 23768402.86624874, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-09}
0.547 (+/-0.300) for {'C': 23768402.86624874, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-08}
0.598 (+/-0.302) for {'C': 23768402.86624874, 'kernel': 'rbf', 'gamma': 2.754228703338175e-07}
0.621 (+/-0.318) for {'C': 23768402.86624874, 'kernel': 'rbf', 'gamma': 2.7542287033381744e-06}
0.658 (+/-0.329) for {'C': 23768402.86624874, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-05}
0.634 (+/-0.308) for {'C': 23768402.86624874, 'kernel': 'rbf', 'gamma': 0.00027542287033381743}
0.614 (+/-0.327) for {'C': 23768402.86624874, 'kernel': 'rbf', 'gamma': 0.0027542287033381747}
0.614 (+/-0.327) for {'C': 23768402.86624874, 'kernel': 'rbf', 'gamma': 0.027542287033381744}
0.614 (+/-0.308) for {'C': 23768402.86624874, 'kernel': 'rbf', 'gamma': 0.27542287033381746}
0.672 (+/-0.451) for {'C': 23768402.86624874, 'kernel': 'rbf', 'gamma': 2.754228703338169}
0.496 (+/-0.001) for {'C': 24210290.467361808, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-10}
0.747 (+/-0.502) for {'C': 24210290.467361808, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-09}
0.548 (+/-0.299) for {'C': 24210290.467361808, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-08}
0.600 (+/-0.302) for {'C': 24210290.467361808, 'kernel': 'rbf', 'gamma': 2.754228703338175e-07}
0.621 (+/-0.318) for {'C': 24210290.467361808, 'kernel': 'rbf', 'gamma': 2.7542287033381744e-06}
0.660 (+/-0.327) for {'C': 24210290.467361808, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-05}
0.634 (+/-0.308) for {'C': 24210290.467361808, 'kernel': 'rbf', 'gamma': 0.00027542287033381743}
0.614 (+/-0.327) for {'C': 24210290.467361808, 'kernel': 'rbf', 'gamma': 0.0027542287033381747}
0.614 (+/-0.327) for {'C': 24210290.467361808, 'kernel': 'rbf', 'gamma': 0.027542287033381744}
0.614 (+/-0.308) for {'C': 24210290.467361808, 'kernel': 'rbf', 'gamma': 0.27542287033381746}
0.672 (+/-0.451) for {'C': 24210290.467361808, 'kernel': 'rbf', 'gamma': 2.754228703338169}
0.496 (+/-0.001) for {'C': 24660393.37234341, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-10}
0.747 (+/-0.502) for {'C': 24660393.37234341, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-09}
0.547 (+/-0.300) for {'C': 24660393.37234341, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-08}
0.598 (+/-0.302) for {'C': 24660393.37234341, 'kernel': 'rbf', 'gamma': 2.754228703338175e-07}
0.621 (+/-0.318) for {'C': 24660393.37234341, 'kernel': 'rbf', 'gamma': 2.7542287033381744e-06}
0.660 (+/-0.327) for {'C': 24660393.37234341, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-05}
0.634 (+/-0.308) for {'C': 24660393.37234341, 'kernel': 'rbf', 'gamma': 0.00027542287033381743}
0.614 (+/-0.327) for {'C': 24660393.37234341, 'kernel': 'rbf', 'gamma': 0.0027542287033381747}
0.614 (+/-0.327) for {'C': 24660393.37234341, 'kernel': 'rbf', 'gamma': 0.027542287033381744}
0.614 (+/-0.308) for {'C': 24660393.37234341, 'kernel': 'rbf', 'gamma': 0.27542287033381746}
0.672 (+/-0.451) for {'C': 24660393.37234341, 'kernel': 'rbf', 'gamma': 2.754228703338169}
0.496 (+/-0.001) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-10}
0.747 (+/-0.502) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-09}
0.547 (+/-0.300) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-08}
0.598 (+/-0.302) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.754228703338175e-07}
0.621 (+/-0.318) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.7542287033381744e-06}
0.660 (+/-0.327) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-05}
0.634 (+/-0.308) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.00027542287033381743}
0.614 (+/-0.327) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.0027542287033381747}
0.614 (+/-0.327) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.027542287033381744}
0.614 (+/-0.308) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.27542287033381746}
0.672 (+/-0.451) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.754228703338169}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 1.0, 'kernel': 'linear'}
0.673 (+/-0.377) for {'C': 10.0, 'kernel': 'linear'}
0.637 (+/-0.310) for {'C': 100.0, 'kernel': 'linear'}
0.622 (+/-0.319) for {'C': 1000.0, 'kernel': 'linear'}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'linear'}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.20 0.17 0.18 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.500 (+/-0.000) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-10}
0.617 (+/-0.238) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-09}
0.470 (+/-0.282) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-08}
0.652 (+/-0.302) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 2.754228703338175e-07}
0.654 (+/-0.311) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 2.7542287033381744e-06}
0.664 (+/-0.224) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-05}
0.638 (+/-0.237) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 0.00027542287033381743}
0.588 (+/-0.233) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 0.0027542287033381747}
0.588 (+/-0.232) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 0.027542287033381744}
0.614 (+/-0.237) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 0.27542287033381746}
0.590 (+/-0.231) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 2.754228703338169}
0.500 (+/-0.000) for {'C': 21281390.459827095, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-10}
0.617 (+/-0.238) for {'C': 21281390.459827095, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-09}
0.465 (+/-0.288) for {'C': 21281390.459827095, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-08}
0.652 (+/-0.302) for {'C': 21281390.459827095, 'kernel': 'rbf', 'gamma': 2.754228703338175e-07}
0.654 (+/-0.311) for {'C': 21281390.459827095, 'kernel': 'rbf', 'gamma': 2.7542287033381744e-06}
0.664 (+/-0.224) for {'C': 21281390.459827095, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-05}
0.638 (+/-0.237) for {'C': 21281390.459827095, 'kernel': 'rbf', 'gamma': 0.00027542287033381743}
0.588 (+/-0.233) for {'C': 21281390.459827095, 'kernel': 'rbf', 'gamma': 0.0027542287033381747}
0.588 (+/-0.232) for {'C': 21281390.459827095, 'kernel': 'rbf', 'gamma': 0.027542287033381744}
0.614 (+/-0.237) for {'C': 21281390.459827095, 'kernel': 'rbf', 'gamma': 0.27542287033381746}
0.590 (+/-0.231) for {'C': 21281390.459827095, 'kernel': 'rbf', 'gamma': 2.754228703338169}
0.500 (+/-0.000) for {'C': 21677041.04819691, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-10}
0.617 (+/-0.238) for {'C': 21677041.04819691, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-09}
0.468 (+/-0.283) for {'C': 21677041.04819691, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-08}
0.652 (+/-0.302) for {'C': 21677041.04819691, 'kernel': 'rbf', 'gamma': 2.754228703338175e-07}
0.654 (+/-0.311) for {'C': 21677041.04819691, 'kernel': 'rbf', 'gamma': 2.7542287033381744e-06}
0.664 (+/-0.224) for {'C': 21677041.04819691, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-05}
0.638 (+/-0.237) for {'C': 21677041.04819691, 'kernel': 'rbf', 'gamma': 0.00027542287033381743}
0.588 (+/-0.233) for {'C': 21677041.04819691, 'kernel': 'rbf', 'gamma': 0.0027542287033381747}
0.588 (+/-0.232) for {'C': 21677041.04819691, 'kernel': 'rbf', 'gamma': 0.027542287033381744}
0.614 (+/-0.237) for {'C': 21677041.04819691, 'kernel': 'rbf', 'gamma': 0.27542287033381746}
0.590 (+/-0.231) for {'C': 21677041.04819691, 'kernel': 'rbf', 'gamma': 2.754228703338169}
0.500 (+/-0.000) for {'C': 22080047.33018901, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-10}
0.617 (+/-0.238) for {'C': 22080047.33018901, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-09}
0.463 (+/-0.290) for {'C': 22080047.33018901, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-08}
0.653 (+/-0.303) for {'C': 22080047.33018901, 'kernel': 'rbf', 'gamma': 2.754228703338175e-07}
0.654 (+/-0.311) for {'C': 22080047.33018901, 'kernel': 'rbf', 'gamma': 2.7542287033381744e-06}
0.689 (+/-0.300) for {'C': 22080047.33018901, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-05}
0.638 (+/-0.237) for {'C': 22080047.33018901, 'kernel': 'rbf', 'gamma': 0.00027542287033381743}
0.588 (+/-0.233) for {'C': 22080047.33018901, 'kernel': 'rbf', 'gamma': 0.0027542287033381747}
0.588 (+/-0.232) for {'C': 22080047.33018901, 'kernel': 'rbf', 'gamma': 0.027542287033381744}
0.614 (+/-0.237) for {'C': 22080047.33018901, 'kernel': 'rbf', 'gamma': 0.27542287033381746}
0.590 (+/-0.231) for {'C': 22080047.33018901, 'kernel': 'rbf', 'gamma': 2.754228703338169}
0.500 (+/-0.000) for {'C': 22490546.058357812, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-10}
0.617 (+/-0.238) for {'C': 22490546.058357812, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-09}
0.464 (+/-0.286) for {'C': 22490546.058357812, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-08}
0.652 (+/-0.302) for {'C': 22490546.058357812, 'kernel': 'rbf', 'gamma': 2.754228703338175e-07}
0.654 (+/-0.311) for {'C': 22490546.058357812, 'kernel': 'rbf', 'gamma': 2.7542287033381744e-06}
0.689 (+/-0.300) for {'C': 22490546.058357812, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-05}
0.638 (+/-0.237) for {'C': 22490546.058357812, 'kernel': 'rbf', 'gamma': 0.00027542287033381743}
0.588 (+/-0.233) for {'C': 22490546.058357812, 'kernel': 'rbf', 'gamma': 0.0027542287033381747}
0.588 (+/-0.232) for {'C': 22490546.058357812, 'kernel': 'rbf', 'gamma': 0.027542287033381744}
0.614 (+/-0.237) for {'C': 22490546.058357812, 'kernel': 'rbf', 'gamma': 0.27542287033381746}
0.590 (+/-0.231) for {'C': 22490546.058357812, 'kernel': 'rbf', 'gamma': 2.754228703338169}
0.500 (+/-0.000) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-10}
0.617 (+/-0.238) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-09}
0.487 (+/-0.291) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-08}
0.652 (+/-0.302) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.754228703338175e-07}
0.654 (+/-0.311) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.7542287033381744e-06}
0.689 (+/-0.300) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-05}
0.638 (+/-0.237) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 0.00027542287033381743}
0.588 (+/-0.233) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 0.0027542287033381747}
0.588 (+/-0.232) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 0.027542287033381744}
0.614 (+/-0.237) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 0.27542287033381746}
0.590 (+/-0.231) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 2.754228703338169}
0.500 (+/-0.000) for {'C': 23334580.622810014, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-10}
0.617 (+/-0.238) for {'C': 23334580.622810014, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-09}
0.463 (+/-0.289) for {'C': 23334580.622810014, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-08}
0.653 (+/-0.303) for {'C': 23334580.622810014, 'kernel': 'rbf', 'gamma': 2.754228703338175e-07}
0.654 (+/-0.311) for {'C': 23334580.622810014, 'kernel': 'rbf', 'gamma': 2.7542287033381744e-06}
0.689 (+/-0.300) for {'C': 23334580.622810014, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-05}
0.638 (+/-0.237) for {'C': 23334580.622810014, 'kernel': 'rbf', 'gamma': 0.00027542287033381743}
0.588 (+/-0.233) for {'C': 23334580.622810014, 'kernel': 'rbf', 'gamma': 0.0027542287033381747}
0.588 (+/-0.232) for {'C': 23334580.622810014, 'kernel': 'rbf', 'gamma': 0.027542287033381744}
0.614 (+/-0.237) for {'C': 23334580.622810014, 'kernel': 'rbf', 'gamma': 0.27542287033381746}
0.590 (+/-0.231) for {'C': 23334580.622810014, 'kernel': 'rbf', 'gamma': 2.754228703338169}
0.500 (+/-0.000) for {'C': 23768402.86624874, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-10}
0.617 (+/-0.238) for {'C': 23768402.86624874, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-09}
0.461 (+/-0.290) for {'C': 23768402.86624874, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-08}
0.652 (+/-0.302) for {'C': 23768402.86624874, 'kernel': 'rbf', 'gamma': 2.754228703338175e-07}
0.654 (+/-0.311) for {'C': 23768402.86624874, 'kernel': 'rbf', 'gamma': 2.7542287033381744e-06}
0.664 (+/-0.317) for {'C': 23768402.86624874, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-05}
0.638 (+/-0.237) for {'C': 23768402.86624874, 'kernel': 'rbf', 'gamma': 0.00027542287033381743}
0.588 (+/-0.233) for {'C': 23768402.86624874, 'kernel': 'rbf', 'gamma': 0.0027542287033381747}
0.588 (+/-0.232) for {'C': 23768402.86624874, 'kernel': 'rbf', 'gamma': 0.027542287033381744}
0.614 (+/-0.237) for {'C': 23768402.86624874, 'kernel': 'rbf', 'gamma': 0.27542287033381746}
0.590 (+/-0.231) for {'C': 23768402.86624874, 'kernel': 'rbf', 'gamma': 2.754228703338169}
0.500 (+/-0.000) for {'C': 24210290.467361808, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-10}
0.617 (+/-0.238) for {'C': 24210290.467361808, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-09}
0.483 (+/-0.294) for {'C': 24210290.467361808, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-08}
0.653 (+/-0.303) for {'C': 24210290.467361808, 'kernel': 'rbf', 'gamma': 2.754228703338175e-07}
0.654 (+/-0.311) for {'C': 24210290.467361808, 'kernel': 'rbf', 'gamma': 2.7542287033381744e-06}
0.664 (+/-0.317) for {'C': 24210290.467361808, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-05}
0.638 (+/-0.237) for {'C': 24210290.467361808, 'kernel': 'rbf', 'gamma': 0.00027542287033381743}
0.588 (+/-0.233) for {'C': 24210290.467361808, 'kernel': 'rbf', 'gamma': 0.0027542287033381747}
0.588 (+/-0.232) for {'C': 24210290.467361808, 'kernel': 'rbf', 'gamma': 0.027542287033381744}
0.614 (+/-0.237) for {'C': 24210290.467361808, 'kernel': 'rbf', 'gamma': 0.27542287033381746}
0.590 (+/-0.231) for {'C': 24210290.467361808, 'kernel': 'rbf', 'gamma': 2.754228703338169}
0.500 (+/-0.000) for {'C': 24660393.37234341, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-10}
0.617 (+/-0.238) for {'C': 24660393.37234341, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-09}
0.459 (+/-0.292) for {'C': 24660393.37234341, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-08}
0.652 (+/-0.302) for {'C': 24660393.37234341, 'kernel': 'rbf', 'gamma': 2.754228703338175e-07}
0.654 (+/-0.311) for {'C': 24660393.37234341, 'kernel': 'rbf', 'gamma': 2.7542287033381744e-06}
0.664 (+/-0.317) for {'C': 24660393.37234341, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-05}
0.638 (+/-0.237) for {'C': 24660393.37234341, 'kernel': 'rbf', 'gamma': 0.00027542287033381743}
0.588 (+/-0.233) for {'C': 24660393.37234341, 'kernel': 'rbf', 'gamma': 0.0027542287033381747}
0.588 (+/-0.232) for {'C': 24660393.37234341, 'kernel': 'rbf', 'gamma': 0.027542287033381744}
0.614 (+/-0.237) for {'C': 24660393.37234341, 'kernel': 'rbf', 'gamma': 0.27542287033381746}
0.590 (+/-0.231) for {'C': 24660393.37234341, 'kernel': 'rbf', 'gamma': 2.754228703338169}
0.500 (+/-0.000) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-10}
0.617 (+/-0.238) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-09}
0.458 (+/-0.293) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-08}
0.652 (+/-0.302) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.754228703338175e-07}
0.654 (+/-0.311) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.7542287033381744e-06}
0.664 (+/-0.317) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-05}
0.638 (+/-0.237) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.00027542287033381743}
0.588 (+/-0.233) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.0027542287033381747}
0.588 (+/-0.232) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.027542287033381744}
0.614 (+/-0.237) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.27542287033381746}
0.590 (+/-0.231) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.754228703338169}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.663 (+/-0.316) for {'C': 10.0, 'kernel': 'linear'}
0.662 (+/-0.315) for {'C': 100.0, 'kernel': 'linear'}
0.613 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
0.588 (+/-0.233) for {'C': 10000.0, 'kernel': 'linear'}
0.588 (+/-0.233) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.10 0.17 0.12 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 22490546.058357812, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-05}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.688941792398384
第2轮loop中,tunemodel函数得到的最优参数是: ([{'C': array([22080047.33018901, 22161543.25959723, 22243339.98485111,
22325438.61616745, 22407840.26786055, 22490546.05835781,
22573557.11021449, 22656874.55012912, 22740499.50895897,
22824433.12173501, 22908676.52767773]), 'kernel': ['rbf'], 'gamma': array([2.75422870e-06, 4.36515832e-06, 6.91830971e-06, 1.09647820e-05,
1.73780083e-05, 2.75422870e-05, 4.36515832e-05, 6.91830971e-05,
1.09647820e-04, 1.73780083e-04, 2.75422870e-04])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}], {'C': 22490546.058357812, 'kernel': 'rbf', 'gamma': 2.7542287033381745e-05}, 0.688941792398384)
这是第2次迭代微调C和gamma。
第2次迭代,得到delta: [4.18130469e+05 5.08219768e-20]
SVC模型clf_op_iter的参数是: {'kernel': 'rbf', 'decision_function_shape': 'ovr', 'class_weight': {-1: 15}, 'tol': 0.001, 'gamma': 2.7542287033381745e-05, 'random_state': 0, 'cache_size': 200, 'verbose': False, 'degree': 3, 'C': 22490546.058357812, 'probability': False, 'shrinking': True, 'coef0': 0.0, 'max_iter': -1}
训练模型SVC对训练样本的预测准确率: 0.9581856839121191
测试集中,预测为舞弊样本的有: (array([1252, 1255], dtype=int64),)
测试集中,实际为舞弊样本的有: (array([1246, 1247, 1248, 1249, 1250, 1251, 1252, 1253, 1254, 1255, 1256],
dtype=int64),)
预测的舞弊样本数目: 2
训练模型SVC对测试样本的预测准确率: 0.9043231750531537
以上是第27次特征筛选。
第27次特征筛选,AUC值是: 0.5909090909090908
X_train_iter_svc.shape is: (1257, 25)
X_test_iter_svc.shape is: (1257, 25)
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.639 (+/-0.326) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.747 (+/-0.449) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.17 0.17 0.17 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.614 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.641 (+/-0.236) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.17 0.17 0.17 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6413461538461539
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.748 (+/-0.449) for {'C': 1.0, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.624 (+/-0.318) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.20 0.17 0.18 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.614 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.20 0.17 0.18 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.99 0.99 629
本轮grid search结果,得到最好的参数选择是: {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6663461538461538
RBF的粗grid search最佳超参: {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001} RBF的粗grid search最高分值: 0.6413461538461539
linear的粗grid search最佳超参: {'C': 1.0, 'kernel': 'linear'} linear的粗grid search最高分值: 0.6663461538461538
粗grid search得到的parameter是:
[{'C': array([1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02, 1.e+03, 1.e+04, 1.e+05,
1.e+06, 1.e+07, 1.e+08]), 'kernel': ['rbf'], 'gamma': array([1.e-08, 1.e-07, 1.e-06, 1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01,
1.e+00, 1.e+01, 1.e+02])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.326) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.449) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.639 (+/-0.326) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.631 (+/-0.327) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.449) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.672 (+/-0.392) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.610 (+/-0.326) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.326) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.748 (+/-0.449) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.375) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.656 (+/-0.312) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.626 (+/-0.318) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.326) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.747 (+/-0.502) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.748 (+/-0.449) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.706 (+/-0.383) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.656 (+/-0.312) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.626 (+/-0.318) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.797 (+/-0.492) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.798 (+/-0.437) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.714 (+/-0.418) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.656 (+/-0.312) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.614 (+/-0.327) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.626 (+/-0.318) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.326) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.649 (+/-0.778) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.797 (+/-0.492) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.676 (+/-0.294) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.632 (+/-0.311) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.643 (+/-0.306) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.614 (+/-0.327) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.626 (+/-0.318) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.326) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.699 (+/-0.797) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.797 (+/-0.492) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.659 (+/-0.313) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.628 (+/-0.312) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.626 (+/-0.318) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.614 (+/-0.327) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.626 (+/-0.318) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.326) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'linear'}
0.706 (+/-0.383) for {'C': 10.0, 'kernel': 'linear'}
0.641 (+/-0.311) for {'C': 100.0, 'kernel': 'linear'}
0.624 (+/-0.318) for {'C': 1000.0, 'kernel': 'linear'}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'linear'}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.006) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.641 (+/-0.236) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.641 (+/-0.236) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.589 (+/-0.230) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.316) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.641 (+/-0.236) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.639 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.617 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.666 (+/-0.316) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.316) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.639 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.588 (+/-0.232) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.642 (+/-0.236) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.683 (+/-0.296) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.666 (+/-0.317) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.664 (+/-0.317) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.588 (+/-0.232) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.588 (+/-0.233) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.239) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.617 (+/-0.238) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.642 (+/-0.236) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.681 (+/-0.295) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.660 (+/-0.318) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.639 (+/-0.237) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.588 (+/-0.233) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.588 (+/-0.233) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.239) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.642 (+/-0.236) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.642 (+/-0.236) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.664 (+/-0.316) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.660 (+/-0.315) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.613 (+/-0.241) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.588 (+/-0.233) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.588 (+/-0.233) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.239) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'linear'}
0.666 (+/-0.315) for {'C': 10.0, 'kernel': 'linear'}
0.663 (+/-0.316) for {'C': 100.0, 'kernel': 'linear'}
0.614 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
0.588 (+/-0.233) for {'C': 10000.0, 'kernel': 'linear'}
0.588 (+/-0.233) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6830128205128205
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.698 (+/-0.383) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.629 (+/-0.329) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.714 (+/-0.359) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.618 (+/-0.322) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.631 (+/-0.327) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.723 (+/-0.358) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.661 (+/-0.384) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.610 (+/-0.326) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.326) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.714 (+/-0.351) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.660 (+/-0.385) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.656 (+/-0.312) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.626 (+/-0.318) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.326) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.747 (+/-0.502) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.797 (+/-0.492) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.710 (+/-0.363) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.662 (+/-0.384) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.656 (+/-0.312) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.626 (+/-0.318) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.024) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.797 (+/-0.492) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.658 (+/-0.290) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.658 (+/-0.388) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.656 (+/-0.312) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.614 (+/-0.327) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.626 (+/-0.318) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.326) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.699 (+/-0.797) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.681 (+/-0.372) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.646 (+/-0.298) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.628 (+/-0.312) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.643 (+/-0.306) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.614 (+/-0.327) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.626 (+/-0.318) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.326) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.699 (+/-0.797) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.668 (+/-0.371) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.625 (+/-0.315) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.628 (+/-0.312) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.626 (+/-0.318) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.614 (+/-0.327) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.626 (+/-0.318) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.326) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 1.0, 'kernel': 'linear'}
0.671 (+/-0.378) for {'C': 10.0, 'kernel': 'linear'}
0.638 (+/-0.310) for {'C': 100.0, 'kernel': 'linear'}
0.624 (+/-0.318) for {'C': 1000.0, 'kernel': 'linear'}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'linear'}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.006) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.666 (+/-0.315) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.612 (+/-0.240) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.682 (+/-0.295) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.610 (+/-0.241) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.682 (+/-0.295) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.661 (+/-0.314) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.589 (+/-0.230) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.682 (+/-0.294) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.660 (+/-0.313) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.639 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.617 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.642 (+/-0.236) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.682 (+/-0.295) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.661 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.639 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.588 (+/-0.232) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.523 (+/-0.138) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.642 (+/-0.236) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.681 (+/-0.292) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.658 (+/-0.315) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.664 (+/-0.317) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.588 (+/-0.232) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.588 (+/-0.233) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.239) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.642 (+/-0.236) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.641 (+/-0.235) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.677 (+/-0.293) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.659 (+/-0.316) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.639 (+/-0.237) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.588 (+/-0.233) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.588 (+/-0.233) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.239) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.642 (+/-0.236) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.640 (+/-0.234) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.658 (+/-0.314) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.660 (+/-0.315) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.613 (+/-0.241) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.588 (+/-0.233) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.588 (+/-0.233) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.239) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.239) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.663 (+/-0.316) for {'C': 10.0, 'kernel': 'linear'}
0.663 (+/-0.316) for {'C': 100.0, 'kernel': 'linear'}
0.614 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
0.588 (+/-0.233) for {'C': 10000.0, 'kernel': 'linear'}
0.588 (+/-0.233) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.17 0.17 0.17 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6823717948717949
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.496 (+/-0.001) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.747 (+/-0.502) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.747 (+/-0.502) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.747 (+/-0.502) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.747 (+/-0.502) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.747 (+/-0.502) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.747 (+/-0.502) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.747 (+/-0.502) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.747 (+/-0.502) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.722 (+/-0.473) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.748 (+/-0.449) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.747 (+/-0.502) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.747 (+/-0.502) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.747 (+/-0.502) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.747 (+/-0.502) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.747 (+/-0.502) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.747 (+/-0.502) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.747 (+/-0.502) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.747 (+/-0.502) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.797 (+/-0.492) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.748 (+/-0.449) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.773 (+/-0.423) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.747 (+/-0.502) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.747 (+/-0.502) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.797 (+/-0.492) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.747 (+/-0.502) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.747 (+/-0.502) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.747 (+/-0.502) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.747 (+/-0.502) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.797 (+/-0.492) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.748 (+/-0.449) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.731 (+/-0.422) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.723 (+/-0.423) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.747 (+/-0.502) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.797 (+/-0.492) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.797 (+/-0.492) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.747 (+/-0.502) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.797 (+/-0.492) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.797 (+/-0.492) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.722 (+/-0.473) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.748 (+/-0.449) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.723 (+/-0.423) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.723 (+/-0.423) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.698 (+/-0.383) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.747 (+/-0.502) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.797 (+/-0.492) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.797 (+/-0.492) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.797 (+/-0.492) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.797 (+/-0.492) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.798 (+/-0.492) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.697 (+/-0.437) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.731 (+/-0.422) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.673 (+/-0.330) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.714 (+/-0.418) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.697 (+/-0.375) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.797 (+/-0.492) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.797 (+/-0.492) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.798 (+/-0.492) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.797 (+/-0.492) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.748 (+/-0.449) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.798 (+/-0.437) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.722 (+/-0.417) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.689 (+/-0.382) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.698 (+/-0.383) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.689 (+/-0.375) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.714 (+/-0.418) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.797 (+/-0.492) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.798 (+/-0.492) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.798 (+/-0.492) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.797 (+/-0.492) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.748 (+/-0.449) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.798 (+/-0.437) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.689 (+/-0.374) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.698 (+/-0.383) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.656 (+/-0.312) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.681 (+/-0.373) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.708 (+/-0.423) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.797 (+/-0.492) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.798 (+/-0.492) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.773 (+/-0.474) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.797 (+/-0.492) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.774 (+/-0.458) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.694 (+/-0.354) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.689 (+/-0.375) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.706 (+/-0.383) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.681 (+/-0.380) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.671 (+/-0.379) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.671 (+/-0.380) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.797 (+/-0.492) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.798 (+/-0.492) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.764 (+/-0.478) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.714 (+/-0.418) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.691 (+/-0.364) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.694 (+/-0.354) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.689 (+/-0.375) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.660 (+/-0.318) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.673 (+/-0.381) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.670 (+/-0.381) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.643 (+/-0.320) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.797 (+/-0.492) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.798 (+/-0.492) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.806 (+/-0.473) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.702 (+/-0.420) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.684 (+/-0.353) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.669 (+/-0.295) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.698 (+/-0.376) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.652 (+/-0.313) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.665 (+/-0.383) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.669 (+/-0.381) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.634 (+/-0.311) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.797 (+/-0.492) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.798 (+/-0.492) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.753 (+/-0.491) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.702 (+/-0.421) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.666 (+/-0.354) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.676 (+/-0.294) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.681 (+/-0.373) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.653 (+/-0.329) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.630 (+/-0.313) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.668 (+/-0.382) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.632 (+/-0.311) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'linear'}
0.706 (+/-0.383) for {'C': 10.0, 'kernel': 'linear'}
0.641 (+/-0.311) for {'C': 100.0, 'kernel': 'linear'}
0.624 (+/-0.318) for {'C': 1000.0, 'kernel': 'linear'}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'linear'}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.500 (+/-0.000) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.617 (+/-0.238) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.617 (+/-0.238) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.617 (+/-0.238) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.617 (+/-0.238) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.617 (+/-0.238) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.617 (+/-0.238) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.617 (+/-0.238) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.617 (+/-0.238) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.617 (+/-0.238) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.666 (+/-0.316) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.617 (+/-0.238) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.617 (+/-0.238) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.617 (+/-0.238) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.617 (+/-0.238) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.617 (+/-0.238) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.617 (+/-0.238) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.617 (+/-0.238) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.617 (+/-0.238) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.642 (+/-0.236) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.666 (+/-0.316) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.683 (+/-0.295) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.617 (+/-0.238) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.617 (+/-0.238) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.642 (+/-0.236) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.617 (+/-0.238) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.617 (+/-0.238) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.617 (+/-0.238) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.617 (+/-0.238) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.642 (+/-0.236) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.666 (+/-0.316) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.666 (+/-0.315) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.666 (+/-0.315) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.617 (+/-0.238) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.642 (+/-0.236) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.642 (+/-0.236) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.617 (+/-0.238) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.642 (+/-0.236) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.642 (+/-0.236) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.617 (+/-0.238) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.666 (+/-0.316) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.666 (+/-0.315) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.666 (+/-0.315) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.666 (+/-0.315) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.617 (+/-0.238) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.642 (+/-0.236) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.642 (+/-0.236) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.642 (+/-0.236) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.642 (+/-0.236) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.667 (+/-0.316) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.616 (+/-0.237) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.666 (+/-0.315) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.666 (+/-0.315) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.666 (+/-0.316) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.641 (+/-0.236) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.642 (+/-0.236) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.642 (+/-0.236) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.667 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.642 (+/-0.236) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.666 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.683 (+/-0.296) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.641 (+/-0.235) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.666 (+/-0.315) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.666 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.666 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.666 (+/-0.317) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.642 (+/-0.236) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.667 (+/-0.316) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.667 (+/-0.316) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.642 (+/-0.236) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.666 (+/-0.316) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.683 (+/-0.296) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.641 (+/-0.235) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.666 (+/-0.315) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.665 (+/-0.315) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.665 (+/-0.317) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.664 (+/-0.319) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.642 (+/-0.236) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.667 (+/-0.316) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.667 (+/-0.316) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.642 (+/-0.236) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.682 (+/-0.295) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.682 (+/-0.294) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.666 (+/-0.316) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.666 (+/-0.316) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.664 (+/-0.315) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.664 (+/-0.317) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.663 (+/-0.319) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.642 (+/-0.236) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.667 (+/-0.316) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.666 (+/-0.316) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.641 (+/-0.235) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.682 (+/-0.295) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.698 (+/-0.304) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.665 (+/-0.316) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.665 (+/-0.315) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.663 (+/-0.316) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.662 (+/-0.320) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.661 (+/-0.319) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.642 (+/-0.236) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.667 (+/-0.316) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.683 (+/-0.296) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.641 (+/-0.235) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.681 (+/-0.291) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.697 (+/-0.305) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.665 (+/-0.317) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.664 (+/-0.315) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.660 (+/-0.318) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.661 (+/-0.321) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.661 (+/-0.318) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.642 (+/-0.236) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.667 (+/-0.316) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.657 (+/-0.310) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.666 (+/-0.315) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.680 (+/-0.290) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.681 (+/-0.295) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.664 (+/-0.318) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.664 (+/-0.316) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.659 (+/-0.318) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.660 (+/-0.322) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.660 (+/-0.318) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'linear'}
0.666 (+/-0.315) for {'C': 10.0, 'kernel': 'linear'}
0.663 (+/-0.316) for {'C': 100.0, 'kernel': 'linear'}
0.614 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
0.588 (+/-0.233) for {'C': 10000.0, 'kernel': 'linear'}
0.588 (+/-0.233) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.17 0.17 0.17 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6979156360788193
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.747 (+/-0.502) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.747 (+/-0.502) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.797 (+/-0.492) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.797 (+/-0.492) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.797 (+/-0.492) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.797 (+/-0.492) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.797 (+/-0.492) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.798 (+/-0.492) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.748 (+/-0.449) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.689 (+/-0.382) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.710 (+/-0.363) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.747 (+/-0.502) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.797 (+/-0.492) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.797 (+/-0.492) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.797 (+/-0.492) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.798 (+/-0.492) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.798 (+/-0.492) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.798 (+/-0.492) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.798 (+/-0.437) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.681 (+/-0.372) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.714 (+/-0.351) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.723 (+/-0.358) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.747 (+/-0.502) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.797 (+/-0.492) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.797 (+/-0.492) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.797 (+/-0.492) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.798 (+/-0.492) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.798 (+/-0.492) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.748 (+/-0.449) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.739 (+/-0.398) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.701 (+/-0.351) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.689 (+/-0.374) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.677 (+/-0.374) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.747 (+/-0.502) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.797 (+/-0.492) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.798 (+/-0.492) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.798 (+/-0.492) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.798 (+/-0.437) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.798 (+/-0.437) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.727 (+/-0.397) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.701 (+/-0.351) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.714 (+/-0.351) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.689 (+/-0.382) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.669 (+/-0.373) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.747 (+/-0.502) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.798 (+/-0.492) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.848 (+/-0.460) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.798 (+/-0.492) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.773 (+/-0.416) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.739 (+/-0.392) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.660 (+/-0.327) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.664 (+/-0.317) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.651 (+/-0.308) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.662 (+/-0.376) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.664 (+/-0.383) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.797 (+/-0.492) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.798 (+/-0.492) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.773 (+/-0.474) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.723 (+/-0.417) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.716 (+/-0.401) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.658 (+/-0.290) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.664 (+/-0.317) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.639 (+/-0.299) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.657 (+/-0.377) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.630 (+/-0.313) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.658 (+/-0.388) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.797 (+/-0.492) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.798 (+/-0.492) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.735 (+/-0.455) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.674 (+/-0.376) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.656 (+/-0.369) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.658 (+/-0.290) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.656 (+/-0.330) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.638 (+/-0.310) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.627 (+/-0.313) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.624 (+/-0.316) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.659 (+/-0.387) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.764 (+/-0.478) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.798 (+/-0.492) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.721 (+/-0.463) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.673 (+/-0.391) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.646 (+/-0.317) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.648 (+/-0.284) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.630 (+/-0.313) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.664 (+/-0.382) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.657 (+/-0.389) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.623 (+/-0.316) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.625 (+/-0.314) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.764 (+/-0.478) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.798 (+/-0.492) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.696 (+/-0.439) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.677 (+/-0.432) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.631 (+/-0.289) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.658 (+/-0.292) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.624 (+/-0.315) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.623 (+/-0.313) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.656 (+/-0.389) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.624 (+/-0.316) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.627 (+/-0.313) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.714 (+/-0.418) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.748 (+/-0.449) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.663 (+/-0.390) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.653 (+/-0.381) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.646 (+/-0.289) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.647 (+/-0.297) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.630 (+/-0.326) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.619 (+/-0.315) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.656 (+/-0.389) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.623 (+/-0.316) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.631 (+/-0.311) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.681 (+/-0.372) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.748 (+/-0.449) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.656 (+/-0.393) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.653 (+/-0.380) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.630 (+/-0.279) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.646 (+/-0.298) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.621 (+/-0.318) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.618 (+/-0.316) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.623 (+/-0.316) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.624 (+/-0.316) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.628 (+/-0.312) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 1.0, 'kernel': 'linear'}
0.671 (+/-0.378) for {'C': 10.0, 'kernel': 'linear'}
0.638 (+/-0.310) for {'C': 100.0, 'kernel': 'linear'}
0.624 (+/-0.318) for {'C': 1000.0, 'kernel': 'linear'}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'linear'}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.617 (+/-0.238) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.617 (+/-0.238) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.642 (+/-0.236) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.642 (+/-0.236) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.642 (+/-0.236) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.642 (+/-0.236) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.642 (+/-0.236) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.667 (+/-0.316) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.666 (+/-0.316) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.666 (+/-0.315) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.682 (+/-0.295) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.617 (+/-0.238) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.642 (+/-0.236) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.642 (+/-0.236) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.642 (+/-0.236) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.667 (+/-0.316) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.667 (+/-0.316) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.667 (+/-0.316) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.683 (+/-0.296) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.666 (+/-0.314) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.682 (+/-0.294) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.682 (+/-0.296) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.617 (+/-0.238) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.642 (+/-0.236) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.642 (+/-0.236) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.642 (+/-0.236) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.667 (+/-0.316) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.667 (+/-0.316) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.666 (+/-0.316) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.682 (+/-0.295) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.682 (+/-0.293) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.665 (+/-0.315) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.665 (+/-0.315) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.617 (+/-0.238) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.642 (+/-0.236) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.667 (+/-0.316) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.667 (+/-0.316) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.683 (+/-0.296) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.683 (+/-0.296) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.682 (+/-0.294) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.682 (+/-0.293) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.682 (+/-0.295) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.665 (+/-0.316) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.664 (+/-0.316) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.617 (+/-0.238) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.667 (+/-0.316) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.683 (+/-0.296) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.667 (+/-0.316) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.683 (+/-0.296) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.683 (+/-0.296) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.665 (+/-0.315) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.665 (+/-0.315) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.664 (+/-0.315) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.663 (+/-0.315) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.661 (+/-0.317) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.642 (+/-0.236) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.667 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.667 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.666 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.682 (+/-0.294) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.681 (+/-0.292) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.665 (+/-0.315) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.664 (+/-0.314) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.662 (+/-0.314) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.660 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.658 (+/-0.315) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.642 (+/-0.236) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.667 (+/-0.316) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.666 (+/-0.316) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.665 (+/-0.314) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.679 (+/-0.288) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.681 (+/-0.293) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.664 (+/-0.316) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.663 (+/-0.315) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.659 (+/-0.316) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.656 (+/-0.316) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.658 (+/-0.315) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.641 (+/-0.236) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.667 (+/-0.316) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.665 (+/-0.315) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.665 (+/-0.314) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.695 (+/-0.301) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.680 (+/-0.293) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.661 (+/-0.315) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.661 (+/-0.317) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.656 (+/-0.315) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.656 (+/-0.316) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.658 (+/-0.314) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.641 (+/-0.236) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.667 (+/-0.316) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.656 (+/-0.308) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.663 (+/-0.315) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.678 (+/-0.289) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.696 (+/-0.307) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.659 (+/-0.311) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.657 (+/-0.319) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.656 (+/-0.314) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.656 (+/-0.316) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.659 (+/-0.316) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.641 (+/-0.235) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.666 (+/-0.316) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.656 (+/-0.308) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.663 (+/-0.316) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.678 (+/-0.291) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.694 (+/-0.306) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.656 (+/-0.311) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.655 (+/-0.317) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.656 (+/-0.315) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.656 (+/-0.315) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.660 (+/-0.318) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.641 (+/-0.235) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.666 (+/-0.316) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.655 (+/-0.308) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.663 (+/-0.315) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.677 (+/-0.294) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.677 (+/-0.293) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.655 (+/-0.310) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.653 (+/-0.318) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.656 (+/-0.315) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.656 (+/-0.315) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.659 (+/-0.316) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.663 (+/-0.316) for {'C': 10.0, 'kernel': 'linear'}
0.663 (+/-0.316) for {'C': 100.0, 'kernel': 'linear'}
0.614 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
0.588 (+/-0.233) for {'C': 10000.0, 'kernel': 'linear'}
0.588 (+/-0.233) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.08 0.17 0.11 6
1 0.99 0.98 0.99 623
avg / total 0.98 0.97 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.695832302745486
循环迭代之前,delta is: [2.98107171e+06 2.11758237e-22]
执行tunemodel()函数前,使用的grid search parameter是:
[{'C': array([2511886.43150958, 2754228.70333817, 3019951.72040201,
3311311.21482591, 3630780.54770102, 3981071.70553498,
4365158.32240166, 4786300.92322638, 5248074.60249773,
5754399.37337156, 6309573.44480193]), 'kernel': ['rbf'], 'gamma': array([6.30957344e-07, 6.91830971e-07, 7.58577575e-07, 8.31763771e-07,
9.12010839e-07, 1.00000000e-06, 1.09647820e-06, 1.20226443e-06,
1.31825674e-06, 1.44543977e-06, 1.58489319e-06])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.774 (+/-0.458) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.702 (+/-0.421) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.756 (+/-0.432) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.681 (+/-0.373) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.789 (+/-0.443) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.694 (+/-0.354) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.684 (+/-0.372) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.689 (+/-0.375) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.652 (+/-0.313) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.664 (+/-0.291) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.689 (+/-0.375) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.774 (+/-0.458) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.685 (+/-0.384) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.698 (+/-0.367) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.677 (+/-0.374) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.765 (+/-0.422) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.710 (+/-0.363) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.684 (+/-0.372) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.689 (+/-0.375) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.652 (+/-0.313) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.647 (+/-0.308) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.681 (+/-0.373) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.773 (+/-0.461) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.699 (+/-0.423) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.714 (+/-0.404) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.677 (+/-0.374) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.765 (+/-0.422) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.694 (+/-0.354) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.684 (+/-0.372) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.689 (+/-0.375) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.652 (+/-0.313) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.651 (+/-0.308) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.681 (+/-0.373) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.756 (+/-0.438) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.683 (+/-0.386) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.664 (+/-0.299) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.677 (+/-0.374) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.764 (+/-0.421) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.694 (+/-0.354) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.684 (+/-0.372) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.689 (+/-0.375) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.648 (+/-0.306) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.651 (+/-0.308) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.689 (+/-0.375) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.731 (+/-0.408) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.677 (+/-0.374) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.639 (+/-0.309) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.677 (+/-0.374) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.756 (+/-0.425) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.694 (+/-0.354) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.684 (+/-0.372) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.689 (+/-0.375) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.645 (+/-0.306) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.651 (+/-0.308) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.689 (+/-0.375) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.691 (+/-0.364) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.685 (+/-0.384) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.658 (+/-0.292) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.668 (+/-0.371) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.706 (+/-0.418) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.694 (+/-0.354) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.689 (+/-0.374) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.689 (+/-0.375) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.648 (+/-0.306) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.656 (+/-0.312) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.689 (+/-0.375) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.675 (+/-0.351) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.669 (+/-0.373) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.660 (+/-0.290) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.677 (+/-0.374) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.756 (+/-0.425) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.694 (+/-0.354) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.689 (+/-0.374) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.689 (+/-0.375) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.651 (+/-0.308) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.651 (+/-0.308) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.698 (+/-0.376) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.673 (+/-0.351) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.666 (+/-0.372) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.660 (+/-0.290) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.677 (+/-0.374) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.764 (+/-0.421) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.702 (+/-0.355) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.673 (+/-0.321) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.689 (+/-0.375) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.651 (+/-0.308) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.651 (+/-0.308) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.698 (+/-0.376) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.673 (+/-0.351) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.672 (+/-0.371) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.664 (+/-0.291) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.668 (+/-0.371) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.764 (+/-0.421) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.702 (+/-0.355) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.673 (+/-0.321) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.689 (+/-0.375) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.651 (+/-0.308) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.643 (+/-0.301) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.698 (+/-0.376) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.676 (+/-0.351) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.666 (+/-0.371) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.658 (+/-0.290) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.668 (+/-0.371) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.764 (+/-0.421) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.694 (+/-0.354) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.673 (+/-0.321) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.681 (+/-0.373) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.651 (+/-0.308) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.651 (+/-0.308) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.698 (+/-0.376) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.684 (+/-0.353) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.664 (+/-0.372) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.635 (+/-0.299) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.677 (+/-0.374) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.764 (+/-0.421) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.669 (+/-0.295) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.673 (+/-0.321) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.681 (+/-0.373) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.645 (+/-0.306) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.651 (+/-0.308) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.698 (+/-0.376) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'linear'}
0.706 (+/-0.383) for {'C': 10.0, 'kernel': 'linear'}
0.641 (+/-0.311) for {'C': 100.0, 'kernel': 'linear'}
0.624 (+/-0.318) for {'C': 1000.0, 'kernel': 'linear'}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'linear'}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.14 0.17 0.15 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.682 (+/-0.295) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.666 (+/-0.315) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.682 (+/-0.295) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.666 (+/-0.315) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.683 (+/-0.296) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.682 (+/-0.294) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.666 (+/-0.314) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.666 (+/-0.315) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.665 (+/-0.314) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.681 (+/-0.293) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.666 (+/-0.316) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.682 (+/-0.295) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.666 (+/-0.315) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.682 (+/-0.295) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.665 (+/-0.315) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.699 (+/-0.309) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.699 (+/-0.307) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.666 (+/-0.314) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.666 (+/-0.315) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.665 (+/-0.314) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.665 (+/-0.314) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.665 (+/-0.316) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.682 (+/-0.295) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.666 (+/-0.315) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.682 (+/-0.295) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.665 (+/-0.315) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.699 (+/-0.309) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.682 (+/-0.294) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.666 (+/-0.314) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.666 (+/-0.316) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.665 (+/-0.314) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.665 (+/-0.314) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.665 (+/-0.316) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.682 (+/-0.295) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.665 (+/-0.314) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.681 (+/-0.294) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.665 (+/-0.315) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.683 (+/-0.296) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.698 (+/-0.305) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.666 (+/-0.314) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.666 (+/-0.316) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.665 (+/-0.313) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.665 (+/-0.314) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.666 (+/-0.316) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.682 (+/-0.295) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.665 (+/-0.314) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.664 (+/-0.313) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.665 (+/-0.315) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.682 (+/-0.296) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.698 (+/-0.304) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.666 (+/-0.314) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.666 (+/-0.316) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.665 (+/-0.312) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.665 (+/-0.314) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.665 (+/-0.316) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.682 (+/-0.295) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.666 (+/-0.315) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.681 (+/-0.292) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.665 (+/-0.315) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.666 (+/-0.316) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.698 (+/-0.304) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.666 (+/-0.314) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.666 (+/-0.316) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.665 (+/-0.313) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.665 (+/-0.315) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.665 (+/-0.316) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.681 (+/-0.292) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.665 (+/-0.313) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.681 (+/-0.293) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.665 (+/-0.315) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.682 (+/-0.296) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.698 (+/-0.304) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.666 (+/-0.314) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.666 (+/-0.316) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.665 (+/-0.314) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.665 (+/-0.315) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.666 (+/-0.317) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.681 (+/-0.291) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.665 (+/-0.312) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.681 (+/-0.293) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.665 (+/-0.315) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.683 (+/-0.296) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.698 (+/-0.304) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.666 (+/-0.314) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.666 (+/-0.316) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.665 (+/-0.314) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.665 (+/-0.315) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.666 (+/-0.317) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.681 (+/-0.291) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.665 (+/-0.313) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.681 (+/-0.294) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.665 (+/-0.315) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.683 (+/-0.296) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.698 (+/-0.304) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.666 (+/-0.315) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.666 (+/-0.316) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.665 (+/-0.314) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.664 (+/-0.314) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.666 (+/-0.317) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.681 (+/-0.291) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.665 (+/-0.312) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.681 (+/-0.292) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.665 (+/-0.315) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.682 (+/-0.296) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.698 (+/-0.305) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.665 (+/-0.315) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.665 (+/-0.316) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.665 (+/-0.315) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.665 (+/-0.315) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.665 (+/-0.317) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.681 (+/-0.291) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.665 (+/-0.311) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.656 (+/-0.308) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.665 (+/-0.315) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.682 (+/-0.296) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.697 (+/-0.305) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.665 (+/-0.315) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.665 (+/-0.316) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.664 (+/-0.313) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.665 (+/-0.315) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.665 (+/-0.317) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'linear'}
0.666 (+/-0.315) for {'C': 10.0, 'kernel': 'linear'}
0.663 (+/-0.316) for {'C': 100.0, 'kernel': 'linear'}
0.614 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
0.588 (+/-0.233) for {'C': 10000.0, 'kernel': 'linear'}
0.588 (+/-0.233) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.12 0.17 0.14 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6993589743589744
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.646 (+/-0.317) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.641 (+/-0.306) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.655 (+/-0.295) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.658 (+/-0.329) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.701 (+/-0.351) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.648 (+/-0.284) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.656 (+/-0.312) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.664 (+/-0.326) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.622 (+/-0.298) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.647 (+/-0.289) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.630 (+/-0.313) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.646 (+/-0.317) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.691 (+/-0.355) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.656 (+/-0.293) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.660 (+/-0.327) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.701 (+/-0.351) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.656 (+/-0.291) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.651 (+/-0.308) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.664 (+/-0.326) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.622 (+/-0.298) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.630 (+/-0.302) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.620 (+/-0.304) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.637 (+/-0.301) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.689 (+/-0.357) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.648 (+/-0.287) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.660 (+/-0.327) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.701 (+/-0.351) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.648 (+/-0.284) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.648 (+/-0.306) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.658 (+/-0.329) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.620 (+/-0.299) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.630 (+/-0.302) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.618 (+/-0.305) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.629 (+/-0.292) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.680 (+/-0.355) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.645 (+/-0.287) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.652 (+/-0.322) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.701 (+/-0.351) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.656 (+/-0.291) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.648 (+/-0.306) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.658 (+/-0.329) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.624 (+/-0.300) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.626 (+/-0.302) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.627 (+/-0.314) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.631 (+/-0.291) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.683 (+/-0.353) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.629 (+/-0.300) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.652 (+/-0.322) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.693 (+/-0.350) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.660 (+/-0.290) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.648 (+/-0.306) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.656 (+/-0.330) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.623 (+/-0.301) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.624 (+/-0.302) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.625 (+/-0.315) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.631 (+/-0.289) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.633 (+/-0.298) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.647 (+/-0.285) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.643 (+/-0.316) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.637 (+/-0.299) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.658 (+/-0.292) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.648 (+/-0.306) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.656 (+/-0.330) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.623 (+/-0.301) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.624 (+/-0.302) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.624 (+/-0.315) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.642 (+/-0.288) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.626 (+/-0.298) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.650 (+/-0.284) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.652 (+/-0.322) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.683 (+/-0.353) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.658 (+/-0.292) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.645 (+/-0.306) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.655 (+/-0.331) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.623 (+/-0.301) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.623 (+/-0.302) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.632 (+/-0.324) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.642 (+/-0.288) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.626 (+/-0.298) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.645 (+/-0.284) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.652 (+/-0.322) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.689 (+/-0.357) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.660 (+/-0.299) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.649 (+/-0.313) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.655 (+/-0.331) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.623 (+/-0.301) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.623 (+/-0.302) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.631 (+/-0.325) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.634 (+/-0.280) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.629 (+/-0.298) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.645 (+/-0.284) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.635 (+/-0.299) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.689 (+/-0.357) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.657 (+/-0.302) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.647 (+/-0.314) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.655 (+/-0.331) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.623 (+/-0.301) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.620 (+/-0.303) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.631 (+/-0.325) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.638 (+/-0.281) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.629 (+/-0.298) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.644 (+/-0.285) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.635 (+/-0.299) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.689 (+/-0.357) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.649 (+/-0.296) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.647 (+/-0.314) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.645 (+/-0.318) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.621 (+/-0.302) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.620 (+/-0.304) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.631 (+/-0.325) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.646 (+/-0.289) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.629 (+/-0.298) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.618 (+/-0.288) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.643 (+/-0.307) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.689 (+/-0.357) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.647 (+/-0.297) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.643 (+/-0.315) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.639 (+/-0.314) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.620 (+/-0.303) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.618 (+/-0.304) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.630 (+/-0.326) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 1.0, 'kernel': 'linear'}
0.671 (+/-0.378) for {'C': 10.0, 'kernel': 'linear'}
0.638 (+/-0.310) for {'C': 100.0, 'kernel': 'linear'}
0.624 (+/-0.318) for {'C': 1000.0, 'kernel': 'linear'}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'linear'}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.20 0.17 0.18 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.695 (+/-0.301) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.664 (+/-0.313) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.680 (+/-0.293) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.665 (+/-0.315) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.681 (+/-0.295) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.680 (+/-0.293) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.665 (+/-0.315) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.664 (+/-0.318) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.662 (+/-0.313) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.679 (+/-0.297) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.661 (+/-0.315) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.695 (+/-0.301) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.681 (+/-0.293) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.681 (+/-0.293) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.665 (+/-0.315) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.698 (+/-0.309) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.696 (+/-0.307) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.665 (+/-0.314) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.664 (+/-0.318) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.662 (+/-0.314) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.662 (+/-0.318) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.660 (+/-0.314) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.678 (+/-0.287) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.681 (+/-0.293) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.680 (+/-0.293) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.665 (+/-0.315) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.698 (+/-0.309) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.680 (+/-0.293) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.665 (+/-0.314) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.663 (+/-0.318) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.661 (+/-0.315) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.661 (+/-0.318) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.660 (+/-0.313) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.678 (+/-0.288) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.681 (+/-0.293) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.680 (+/-0.293) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.665 (+/-0.316) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.681 (+/-0.296) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.696 (+/-0.307) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.664 (+/-0.314) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.663 (+/-0.318) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.661 (+/-0.315) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.661 (+/-0.317) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.660 (+/-0.313) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.678 (+/-0.288) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.681 (+/-0.293) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.663 (+/-0.314) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.665 (+/-0.316) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.681 (+/-0.296) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.696 (+/-0.307) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.664 (+/-0.314) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.663 (+/-0.319) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.661 (+/-0.315) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.661 (+/-0.317) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.659 (+/-0.311) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.678 (+/-0.289) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.664 (+/-0.313) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.680 (+/-0.294) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.664 (+/-0.316) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.663 (+/-0.314) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.696 (+/-0.307) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.664 (+/-0.315) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.663 (+/-0.319) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.660 (+/-0.316) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.660 (+/-0.318) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.659 (+/-0.311) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.678 (+/-0.290) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.663 (+/-0.313) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.680 (+/-0.294) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.664 (+/-0.316) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.680 (+/-0.295) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.696 (+/-0.307) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.664 (+/-0.314) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.662 (+/-0.319) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.660 (+/-0.317) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.659 (+/-0.319) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.658 (+/-0.311) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.678 (+/-0.290) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.663 (+/-0.313) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.680 (+/-0.294) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.664 (+/-0.317) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.680 (+/-0.295) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.696 (+/-0.307) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.664 (+/-0.314) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.662 (+/-0.319) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.660 (+/-0.317) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.659 (+/-0.319) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.657 (+/-0.311) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.678 (+/-0.290) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.663 (+/-0.314) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.680 (+/-0.294) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.664 (+/-0.316) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.680 (+/-0.296) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.695 (+/-0.307) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.664 (+/-0.314) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.662 (+/-0.320) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.659 (+/-0.318) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.659 (+/-0.317) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.657 (+/-0.311) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.678 (+/-0.290) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.663 (+/-0.314) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.679 (+/-0.294) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.664 (+/-0.316) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.679 (+/-0.296) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.695 (+/-0.307) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.664 (+/-0.314) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.662 (+/-0.319) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.659 (+/-0.318) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.658 (+/-0.318) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.657 (+/-0.311) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.678 (+/-0.291) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.663 (+/-0.314) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.654 (+/-0.309) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.664 (+/-0.316) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.679 (+/-0.297) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.694 (+/-0.306) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.663 (+/-0.313) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.661 (+/-0.318) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.658 (+/-0.318) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.658 (+/-0.316) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.656 (+/-0.311) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.663 (+/-0.316) for {'C': 10.0, 'kernel': 'linear'}
0.663 (+/-0.316) for {'C': 100.0, 'kernel': 'linear'}
0.614 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
0.588 (+/-0.233) for {'C': 10000.0, 'kernel': 'linear'}
0.588 (+/-0.233) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.14 0.17 0.15 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6975961538461539
第0轮loop中,tunemodel函数得到的最优参数是: ([{'C': array([2511886.43150958, 2558585.88690565, 2606153.5499989 ,
2654605.56197554, 2703958.36410884, 2754228.70333816,
2805433.63795172, 2857590.54337495, 2910717.11806661,
2964831.38952434, 3019951.72040201]), 'kernel': ['rbf'], 'gamma': array([8.31763771e-07, 8.47227414e-07, 8.62978548e-07, 8.79022517e-07,
8.95364766e-07, 9.12010839e-07, 9.28966387e-07, 9.46237161e-07,
9.63829024e-07, 9.81747943e-07, 1.00000000e-06])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}], {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}, 0.6993589743589744)
这是第0次迭代微调C和gamma。
第0次迭代,得到delta: [1.22684300e+06 8.79891606e-08]
执行tunemodel()函数前,使用的grid search parameter是:
[{'C': array([2511886.43150958, 2558585.88690565, 2606153.5499989 ,
2654605.56197554, 2703958.36410884, 2754228.70333816,
2805433.63795172, 2857590.54337495, 2910717.11806661,
2964831.38952434, 3019951.72040201]), 'kernel': ['rbf'], 'gamma': array([8.31763771e-07, 8.47227414e-07, 8.62978548e-07, 8.79022517e-07,
8.95364766e-07, 9.12010839e-07, 9.28966387e-07, 9.46237161e-07,
9.63829024e-07, 9.81747943e-07, 1.00000000e-06])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.681 (+/-0.373) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.693 (+/-0.420) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 8.472274141405952e-07}
0.702 (+/-0.421) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 8.629785477669687e-07}
0.699 (+/-0.423) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 8.790225168308846e-07}
0.773 (+/-0.416) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 8.953647655495937e-07}
0.789 (+/-0.443) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.748 (+/-0.395) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.714 (+/-0.418) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.756 (+/-0.425) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.764 (+/-0.421) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.694 (+/-0.354) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.681 (+/-0.373) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.693 (+/-0.420) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 8.472274141405952e-07}
0.702 (+/-0.421) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 8.629785477669687e-07}
0.699 (+/-0.423) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 8.790225168308846e-07}
0.773 (+/-0.416) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 8.953647655495937e-07}
0.789 (+/-0.443) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.748 (+/-0.395) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.714 (+/-0.418) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.756 (+/-0.425) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.764 (+/-0.421) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.702 (+/-0.355) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.677 (+/-0.374) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.693 (+/-0.420) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 8.472274141405952e-07}
0.702 (+/-0.421) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 8.629785477669687e-07}
0.702 (+/-0.421) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 8.790225168308846e-07}
0.764 (+/-0.421) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 8.953647655495937e-07}
0.789 (+/-0.443) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.748 (+/-0.395) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.714 (+/-0.418) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.756 (+/-0.425) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.764 (+/-0.421) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.702 (+/-0.355) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.677 (+/-0.374) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.693 (+/-0.420) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 8.472274141405952e-07}
0.702 (+/-0.421) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 8.629785477669687e-07}
0.699 (+/-0.423) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 8.790225168308846e-07}
0.764 (+/-0.421) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 8.953647655495937e-07}
0.764 (+/-0.421) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.748 (+/-0.395) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.714 (+/-0.418) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.756 (+/-0.425) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.764 (+/-0.421) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.694 (+/-0.354) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.677 (+/-0.374) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.693 (+/-0.420) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 8.472274141405952e-07}
0.702 (+/-0.421) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 8.629785477669687e-07}
0.699 (+/-0.423) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 8.790225168308846e-07}
0.764 (+/-0.421) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 8.953647655495937e-07}
0.765 (+/-0.422) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.748 (+/-0.395) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.714 (+/-0.418) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.756 (+/-0.425) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.764 (+/-0.421) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.702 (+/-0.363) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.677 (+/-0.374) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.693 (+/-0.420) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 8.472274141405952e-07}
0.702 (+/-0.421) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 8.629785477669687e-07}
0.702 (+/-0.421) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 8.790225168308846e-07}
0.756 (+/-0.425) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 8.953647655495937e-07}
0.765 (+/-0.422) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.748 (+/-0.395) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.714 (+/-0.418) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.756 (+/-0.425) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.764 (+/-0.421) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.710 (+/-0.363) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.677 (+/-0.374) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.693 (+/-0.420) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 8.472274141405952e-07}
0.702 (+/-0.421) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 8.629785477669687e-07}
0.698 (+/-0.424) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 8.790225168308846e-07}
0.764 (+/-0.421) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 8.953647655495937e-07}
0.765 (+/-0.422) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.748 (+/-0.395) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.714 (+/-0.418) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.756 (+/-0.425) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.764 (+/-0.421) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.710 (+/-0.363) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.677 (+/-0.374) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.693 (+/-0.420) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 8.472274141405952e-07}
0.702 (+/-0.421) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 8.629785477669687e-07}
0.702 (+/-0.421) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 8.790225168308846e-07}
0.756 (+/-0.425) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 8.953647655495937e-07}
0.765 (+/-0.422) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.698 (+/-0.383) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.714 (+/-0.418) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.756 (+/-0.425) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.748 (+/-0.395) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.702 (+/-0.355) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.677 (+/-0.374) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.693 (+/-0.420) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 8.472274141405952e-07}
0.706 (+/-0.418) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 8.629785477669687e-07}
0.702 (+/-0.421) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 8.790225168308846e-07}
0.756 (+/-0.425) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 8.953647655495937e-07}
0.765 (+/-0.422) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.698 (+/-0.383) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.714 (+/-0.418) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.756 (+/-0.425) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.764 (+/-0.421) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.694 (+/-0.354) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.677 (+/-0.374) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.693 (+/-0.420) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 8.472274141405952e-07}
0.706 (+/-0.418) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 8.629785477669687e-07}
0.702 (+/-0.421) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 8.790225168308846e-07}
0.756 (+/-0.425) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 8.953647655495937e-07}
0.764 (+/-0.421) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.698 (+/-0.383) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.714 (+/-0.418) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.756 (+/-0.425) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.748 (+/-0.395) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.694 (+/-0.354) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.677 (+/-0.374) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.693 (+/-0.420) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 8.472274141405952e-07}
0.706 (+/-0.418) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 8.629785477669687e-07}
0.699 (+/-0.423) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 8.790225168308846e-07}
0.756 (+/-0.425) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 8.953647655495937e-07}
0.765 (+/-0.422) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.698 (+/-0.383) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.698 (+/-0.383) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.756 (+/-0.425) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.764 (+/-0.421) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.694 (+/-0.354) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'linear'}
0.706 (+/-0.383) for {'C': 10.0, 'kernel': 'linear'}
0.641 (+/-0.311) for {'C': 100.0, 'kernel': 'linear'}
0.624 (+/-0.318) for {'C': 1000.0, 'kernel': 'linear'}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'linear'}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.14 0.17 0.15 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.666 (+/-0.315) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.666 (+/-0.315) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 8.472274141405952e-07}
0.666 (+/-0.315) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 8.629785477669687e-07}
0.666 (+/-0.315) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 8.790225168308846e-07}
0.683 (+/-0.296) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 8.953647655495937e-07}
0.683 (+/-0.296) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.683 (+/-0.295) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.666 (+/-0.316) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.683 (+/-0.296) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.683 (+/-0.296) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.682 (+/-0.294) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.666 (+/-0.315) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.666 (+/-0.315) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 8.472274141405952e-07}
0.666 (+/-0.315) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 8.629785477669687e-07}
0.666 (+/-0.315) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 8.790225168308846e-07}
0.683 (+/-0.296) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 8.953647655495937e-07}
0.683 (+/-0.296) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.683 (+/-0.295) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.666 (+/-0.316) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.683 (+/-0.296) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.683 (+/-0.296) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.698 (+/-0.307) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.666 (+/-0.315) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.666 (+/-0.315) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 8.472274141405952e-07}
0.666 (+/-0.315) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 8.629785477669687e-07}
0.666 (+/-0.315) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 8.790225168308846e-07}
0.683 (+/-0.296) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 8.953647655495937e-07}
0.683 (+/-0.296) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.683 (+/-0.295) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.666 (+/-0.316) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.683 (+/-0.296) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.683 (+/-0.296) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.698 (+/-0.307) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.666 (+/-0.315) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.666 (+/-0.315) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 8.472274141405952e-07}
0.666 (+/-0.315) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 8.629785477669687e-07}
0.666 (+/-0.315) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 8.790225168308846e-07}
0.683 (+/-0.296) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 8.953647655495937e-07}
0.683 (+/-0.296) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.683 (+/-0.295) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.666 (+/-0.316) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.683 (+/-0.296) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.683 (+/-0.296) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.682 (+/-0.294) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.665 (+/-0.315) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.666 (+/-0.315) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 8.472274141405952e-07}
0.666 (+/-0.315) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 8.629785477669687e-07}
0.666 (+/-0.315) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 8.790225168308846e-07}
0.683 (+/-0.296) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 8.953647655495937e-07}
0.699 (+/-0.309) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.683 (+/-0.295) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.666 (+/-0.316) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.683 (+/-0.296) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.683 (+/-0.296) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.682 (+/-0.295) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.665 (+/-0.315) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.666 (+/-0.315) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 8.472274141405952e-07}
0.666 (+/-0.315) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 8.629785477669687e-07}
0.666 (+/-0.315) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 8.790225168308846e-07}
0.682 (+/-0.296) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 8.953647655495937e-07}
0.699 (+/-0.309) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.683 (+/-0.295) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.666 (+/-0.316) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.683 (+/-0.296) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.683 (+/-0.296) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.699 (+/-0.307) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.665 (+/-0.315) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.666 (+/-0.315) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 8.472274141405952e-07}
0.666 (+/-0.315) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 8.629785477669687e-07}
0.665 (+/-0.315) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 8.790225168308846e-07}
0.683 (+/-0.296) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 8.953647655495937e-07}
0.699 (+/-0.309) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.683 (+/-0.295) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.666 (+/-0.316) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.683 (+/-0.296) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.683 (+/-0.296) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.699 (+/-0.307) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.665 (+/-0.315) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.666 (+/-0.315) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 8.472274141405952e-07}
0.666 (+/-0.315) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 8.629785477669687e-07}
0.666 (+/-0.315) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 8.790225168308846e-07}
0.682 (+/-0.296) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 8.953647655495937e-07}
0.699 (+/-0.309) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.666 (+/-0.315) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.666 (+/-0.316) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.683 (+/-0.296) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.682 (+/-0.295) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.698 (+/-0.307) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.665 (+/-0.315) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.666 (+/-0.315) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 8.472274141405952e-07}
0.666 (+/-0.315) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 8.629785477669687e-07}
0.666 (+/-0.315) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 8.790225168308846e-07}
0.682 (+/-0.296) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 8.953647655495937e-07}
0.699 (+/-0.309) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.666 (+/-0.315) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.666 (+/-0.316) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.683 (+/-0.296) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.683 (+/-0.296) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.682 (+/-0.294) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.665 (+/-0.315) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.666 (+/-0.315) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 8.472274141405952e-07}
0.666 (+/-0.315) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 8.629785477669687e-07}
0.666 (+/-0.315) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 8.790225168308846e-07}
0.682 (+/-0.296) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 8.953647655495937e-07}
0.683 (+/-0.296) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.666 (+/-0.315) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.666 (+/-0.316) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.683 (+/-0.296) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.682 (+/-0.295) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.682 (+/-0.294) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.665 (+/-0.315) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.666 (+/-0.315) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 8.472274141405952e-07}
0.666 (+/-0.315) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 8.629785477669687e-07}
0.666 (+/-0.315) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 8.790225168308846e-07}
0.682 (+/-0.296) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 8.953647655495937e-07}
0.699 (+/-0.309) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.666 (+/-0.315) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.666 (+/-0.315) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.683 (+/-0.296) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.683 (+/-0.296) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.682 (+/-0.294) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'linear'}
0.666 (+/-0.315) for {'C': 10.0, 'kernel': 'linear'}
0.663 (+/-0.316) for {'C': 100.0, 'kernel': 'linear'}
0.614 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
0.588 (+/-0.233) for {'C': 10000.0, 'kernel': 'linear'}
0.588 (+/-0.233) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.12 0.17 0.14 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6993589743589744
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.658 (+/-0.329) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.652 (+/-0.322) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 8.472274141405952e-07}
0.677 (+/-0.374) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 8.629785477669687e-07}
0.664 (+/-0.374) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 8.790225168308846e-07}
0.740 (+/-0.392) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 8.953647655495937e-07}
0.701 (+/-0.351) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.691 (+/-0.356) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.650 (+/-0.313) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.702 (+/-0.354) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.691 (+/-0.355) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.648 (+/-0.284) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.658 (+/-0.329) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.652 (+/-0.322) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 8.472274141405952e-07}
0.677 (+/-0.374) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 8.629785477669687e-07}
0.664 (+/-0.374) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 8.790225168308846e-07}
0.740 (+/-0.392) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 8.953647655495937e-07}
0.701 (+/-0.351) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.691 (+/-0.356) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.650 (+/-0.313) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.694 (+/-0.353) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.691 (+/-0.355) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.656 (+/-0.291) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.660 (+/-0.327) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.652 (+/-0.322) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 8.472274141405952e-07}
0.677 (+/-0.374) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 8.629785477669687e-07}
0.664 (+/-0.374) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 8.790225168308846e-07}
0.740 (+/-0.392) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 8.953647655495937e-07}
0.709 (+/-0.351) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.687 (+/-0.358) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.650 (+/-0.313) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.684 (+/-0.353) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.691 (+/-0.355) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.656 (+/-0.291) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.660 (+/-0.327) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.652 (+/-0.322) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 8.472274141405952e-07}
0.677 (+/-0.374) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 8.629785477669687e-07}
0.664 (+/-0.374) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 8.790225168308846e-07}
0.731 (+/-0.394) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 8.953647655495937e-07}
0.701 (+/-0.351) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.687 (+/-0.358) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.649 (+/-0.314) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.684 (+/-0.353) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.689 (+/-0.357) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.648 (+/-0.284) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.660 (+/-0.327) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.652 (+/-0.322) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 8.472274141405952e-07}
0.677 (+/-0.374) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 8.629785477669687e-07}
0.664 (+/-0.374) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 8.790225168308846e-07}
0.731 (+/-0.394) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 8.953647655495937e-07}
0.701 (+/-0.351) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.687 (+/-0.358) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.649 (+/-0.314) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.684 (+/-0.353) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.687 (+/-0.358) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.648 (+/-0.284) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.660 (+/-0.327) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.652 (+/-0.322) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 8.472274141405952e-07}
0.677 (+/-0.374) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 8.629785477669687e-07}
0.668 (+/-0.371) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 8.790225168308846e-07}
0.731 (+/-0.394) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 8.953647655495937e-07}
0.701 (+/-0.351) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.687 (+/-0.358) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.649 (+/-0.314) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.684 (+/-0.353) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.687 (+/-0.358) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.656 (+/-0.291) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.660 (+/-0.327) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.652 (+/-0.322) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 8.472274141405952e-07}
0.677 (+/-0.374) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 8.629785477669687e-07}
0.668 (+/-0.371) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 8.790225168308846e-07}
0.731 (+/-0.394) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 8.953647655495937e-07}
0.701 (+/-0.351) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.687 (+/-0.358) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.649 (+/-0.314) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.684 (+/-0.353) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.687 (+/-0.358) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.656 (+/-0.291) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.660 (+/-0.327) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.643 (+/-0.316) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 8.472274141405952e-07}
0.677 (+/-0.374) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 8.629785477669687e-07}
0.664 (+/-0.374) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 8.790225168308846e-07}
0.731 (+/-0.394) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 8.953647655495937e-07}
0.701 (+/-0.351) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.637 (+/-0.307) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.640 (+/-0.308) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.684 (+/-0.353) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.687 (+/-0.358) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.656 (+/-0.291) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.660 (+/-0.327) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.643 (+/-0.316) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 8.472274141405952e-07}
0.681 (+/-0.373) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 8.629785477669687e-07}
0.664 (+/-0.374) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 8.790225168308846e-07}
0.731 (+/-0.394) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 8.953647655495937e-07}
0.701 (+/-0.351) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.636 (+/-0.308) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.640 (+/-0.308) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.684 (+/-0.353) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.687 (+/-0.358) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.648 (+/-0.284) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.660 (+/-0.327) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.643 (+/-0.316) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 8.472274141405952e-07}
0.681 (+/-0.373) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 8.629785477669687e-07}
0.664 (+/-0.374) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 8.790225168308846e-07}
0.731 (+/-0.394) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 8.953647655495937e-07}
0.701 (+/-0.351) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.637 (+/-0.307) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.640 (+/-0.308) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.684 (+/-0.353) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.687 (+/-0.358) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.648 (+/-0.284) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.660 (+/-0.327) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.643 (+/-0.316) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 8.472274141405952e-07}
0.681 (+/-0.373) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 8.629785477669687e-07}
0.668 (+/-0.371) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 8.790225168308846e-07}
0.731 (+/-0.394) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 8.953647655495937e-07}
0.701 (+/-0.351) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.636 (+/-0.308) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.640 (+/-0.308) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.684 (+/-0.353) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.685 (+/-0.360) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.648 (+/-0.284) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 1.0, 'kernel': 'linear'}
0.671 (+/-0.378) for {'C': 10.0, 'kernel': 'linear'}
0.638 (+/-0.310) for {'C': 100.0, 'kernel': 'linear'}
0.624 (+/-0.318) for {'C': 1000.0, 'kernel': 'linear'}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'linear'}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.20 0.17 0.18 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.665 (+/-0.315) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.665 (+/-0.315) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 8.472274141405952e-07}
0.665 (+/-0.316) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 8.629785477669687e-07}
0.665 (+/-0.315) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 8.790225168308846e-07}
0.699 (+/-0.309) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 8.953647655495937e-07}
0.681 (+/-0.295) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.680 (+/-0.292) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.664 (+/-0.312) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.681 (+/-0.293) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.681 (+/-0.293) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.680 (+/-0.293) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.665 (+/-0.315) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.665 (+/-0.315) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 8.472274141405952e-07}
0.665 (+/-0.316) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 8.629785477669687e-07}
0.665 (+/-0.316) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 8.790225168308846e-07}
0.699 (+/-0.310) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 8.953647655495937e-07}
0.681 (+/-0.295) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.680 (+/-0.292) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.664 (+/-0.312) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.681 (+/-0.293) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.681 (+/-0.293) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.696 (+/-0.307) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.665 (+/-0.315) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.665 (+/-0.315) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 8.472274141405952e-07}
0.665 (+/-0.316) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 8.629785477669687e-07}
0.665 (+/-0.316) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 8.790225168308846e-07}
0.699 (+/-0.310) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 8.953647655495937e-07}
0.681 (+/-0.295) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.680 (+/-0.291) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.664 (+/-0.312) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.680 (+/-0.293) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.681 (+/-0.293) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.696 (+/-0.307) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.665 (+/-0.315) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.665 (+/-0.315) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 8.472274141405952e-07}
0.665 (+/-0.316) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 8.629785477669687e-07}
0.665 (+/-0.316) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 8.790225168308846e-07}
0.682 (+/-0.297) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 8.953647655495937e-07}
0.681 (+/-0.295) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.680 (+/-0.291) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.664 (+/-0.311) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.680 (+/-0.293) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.681 (+/-0.293) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.680 (+/-0.293) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.665 (+/-0.315) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.665 (+/-0.315) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 8.472274141405952e-07}
0.665 (+/-0.316) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 8.629785477669687e-07}
0.665 (+/-0.316) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 8.790225168308846e-07}
0.682 (+/-0.297) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 8.953647655495937e-07}
0.698 (+/-0.309) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.680 (+/-0.292) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.664 (+/-0.311) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.680 (+/-0.293) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.681 (+/-0.292) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.680 (+/-0.293) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.665 (+/-0.315) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.665 (+/-0.315) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 8.472274141405952e-07}
0.665 (+/-0.316) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 8.629785477669687e-07}
0.665 (+/-0.316) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 8.790225168308846e-07}
0.682 (+/-0.297) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 8.953647655495937e-07}
0.698 (+/-0.309) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.680 (+/-0.292) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.664 (+/-0.311) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.680 (+/-0.293) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.681 (+/-0.292) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.696 (+/-0.307) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.665 (+/-0.315) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.665 (+/-0.315) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 8.472274141405952e-07}
0.665 (+/-0.316) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 8.629785477669687e-07}
0.665 (+/-0.316) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 8.790225168308846e-07}
0.681 (+/-0.298) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 8.953647655495937e-07}
0.698 (+/-0.309) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.680 (+/-0.292) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.664 (+/-0.311) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.680 (+/-0.293) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.681 (+/-0.292) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.696 (+/-0.307) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.665 (+/-0.315) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.665 (+/-0.315) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 8.472274141405952e-07}
0.665 (+/-0.316) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 8.629785477669687e-07}
0.665 (+/-0.316) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 8.790225168308846e-07}
0.681 (+/-0.298) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 8.953647655495937e-07}
0.698 (+/-0.309) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.663 (+/-0.311) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.663 (+/-0.311) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.680 (+/-0.293) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.681 (+/-0.292) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.696 (+/-0.307) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.665 (+/-0.315) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.665 (+/-0.315) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 8.472274141405952e-07}
0.665 (+/-0.317) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 8.629785477669687e-07}
0.665 (+/-0.316) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 8.790225168308846e-07}
0.681 (+/-0.298) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 8.953647655495937e-07}
0.698 (+/-0.309) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.663 (+/-0.311) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.663 (+/-0.311) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.680 (+/-0.293) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.681 (+/-0.292) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.680 (+/-0.293) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.665 (+/-0.315) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.665 (+/-0.315) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 8.472274141405952e-07}
0.665 (+/-0.317) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 8.629785477669687e-07}
0.665 (+/-0.316) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 8.790225168308846e-07}
0.682 (+/-0.297) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 8.953647655495937e-07}
0.681 (+/-0.296) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.663 (+/-0.312) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.663 (+/-0.311) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.680 (+/-0.293) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.681 (+/-0.292) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.680 (+/-0.293) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.665 (+/-0.315) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.665 (+/-0.315) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 8.472274141405952e-07}
0.665 (+/-0.317) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 8.629785477669687e-07}
0.665 (+/-0.316) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 8.790225168308846e-07}
0.681 (+/-0.298) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 8.953647655495937e-07}
0.698 (+/-0.309) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.663 (+/-0.311) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.663 (+/-0.311) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.680 (+/-0.293) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.680 (+/-0.292) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.680 (+/-0.293) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.663 (+/-0.316) for {'C': 10.0, 'kernel': 'linear'}
0.663 (+/-0.316) for {'C': 100.0, 'kernel': 'linear'}
0.614 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
0.588 (+/-0.233) for {'C': 10000.0, 'kernel': 'linear'}
0.588 (+/-0.233) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 8.953647655495937e-07}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6988782051282051
第1轮loop中,tunemodel函数得到的最优参数是: ([{'C': array([2654605.56197554, 2664403.5277249 , 2674237.65708899,
2684108.08354523, 2694014.94106366, 2703958.36410884,
2713938.48764159, 2723955.44712086, 2734009.37850561,
2744100.41825657, 2754228.70333816]), 'kernel': ['rbf'], 'gamma': array([8.95364766e-07, 8.98669495e-07, 9.01986422e-07, 9.05315592e-07,
9.08657049e-07, 9.12010839e-07, 9.15377008e-07, 9.18755602e-07,
9.22146665e-07, 9.25550245e-07, 9.28966387e-07])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}], {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}, 0.6993589743589744)
这是第1次迭代微调C和gamma。
第1次迭代,得到delta: [50270.33922933 0. ]
SVC模型clf_op_iter的参数是: {'kernel': 'rbf', 'decision_function_shape': 'ovr', 'class_weight': {-1: 15}, 'tol': 0.001, 'gamma': 9.120108393559093e-07, 'random_state': 0, 'cache_size': 200, 'verbose': False, 'degree': 3, 'C': 2703958.364108842, 'probability': False, 'shrinking': True, 'coef0': 0.0, 'max_iter': -1}
训练模型SVC对训练样本的预测准确率: 0.9347980155917789
测试集中,预测为舞弊样本的有: (array([1255], dtype=int64),)
测试集中,实际为舞弊样本的有: (array([1246, 1247, 1248, 1249, 1250, 1251, 1252, 1253, 1254, 1255, 1256],
dtype=int64),)
预测的舞弊样本数目: 1
训练模型SVC对测试样本的预测准确率: 0.8936924167257264
以上是第28次特征筛选。
第28次特征筛选,AUC值是: 0.5454545454545454
X_train_iter_svc.shape is: (1257, 24)
X_test_iter_svc.shape is: (1257, 24)
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.622 (+/-0.336) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.747 (+/-0.449) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.17 0.17 0.17 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.589 (+/-0.231) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.641 (+/-0.236) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.17 0.17 0.17 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6413461538461539
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.748 (+/-0.449) for {'C': 1.0, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.630 (+/-0.310) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.20 0.17 0.18 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.637 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.20 0.17 0.18 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.99 0.99 629
本轮grid search结果,得到最好的参数选择是: {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6663461538461538
RBF的粗grid search最佳超参: {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001} RBF的粗grid search最高分值: 0.6413461538461539
linear的粗grid search最佳超参: {'C': 1.0, 'kernel': 'linear'} linear的粗grid search最高分值: 0.6663461538461538
粗grid search得到的parameter是:
[{'C': array([1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02, 1.e+03, 1.e+04, 1.e+05,
1.e+06, 1.e+07, 1.e+08]), 'kernel': ['rbf'], 'gamma': array([1.e-08, 1.e-07, 1.e-06, 1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01,
1.e+00, 1.e+01, 1.e+02])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.647 (+/-0.333) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.449) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.664 (+/-0.388) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.635 (+/-0.326) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.449) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.375) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.610 (+/-0.326) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.622 (+/-0.336) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.449) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.375) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.625 (+/-0.314) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.610 (+/-0.326) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.622 (+/-0.336) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.747 (+/-0.502) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.748 (+/-0.449) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.375) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.625 (+/-0.314) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.610 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.622 (+/-0.336) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.797 (+/-0.492) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.798 (+/-0.437) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.723 (+/-0.417) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.638 (+/-0.301) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.620 (+/-0.320) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.610 (+/-0.326) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.622 (+/-0.336) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.649 (+/-0.778) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.797 (+/-0.492) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.676 (+/-0.294) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.627 (+/-0.313) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.614 (+/-0.327) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.619 (+/-0.322) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.610 (+/-0.326) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.622 (+/-0.336) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.699 (+/-0.797) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.797 (+/-0.492) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.651 (+/-0.308) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.625 (+/-0.314) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.614 (+/-0.327) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.619 (+/-0.322) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.610 (+/-0.326) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.622 (+/-0.336) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'linear'}
0.706 (+/-0.383) for {'C': 10.0, 'kernel': 'linear'}
0.643 (+/-0.320) for {'C': 100.0, 'kernel': 'linear'}
0.630 (+/-0.310) for {'C': 1000.0, 'kernel': 'linear'}
0.619 (+/-0.321) for {'C': 10000.0, 'kernel': 'linear'}
0.627 (+/-0.313) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.006) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.004) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.641 (+/-0.236) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.240) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.004) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.641 (+/-0.236) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.641 (+/-0.236) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.587 (+/-0.233) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.589 (+/-0.231) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.004) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.641 (+/-0.236) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.641 (+/-0.236) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.636 (+/-0.236) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.588 (+/-0.232) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.589 (+/-0.231) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.004) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.617 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.666 (+/-0.316) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.641 (+/-0.236) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.636 (+/-0.235) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.588 (+/-0.233) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.588 (+/-0.231) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.589 (+/-0.231) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.004) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.642 (+/-0.236) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.683 (+/-0.296) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.666 (+/-0.316) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.662 (+/-0.224) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.612 (+/-0.241) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.588 (+/-0.233) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.588 (+/-0.231) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.589 (+/-0.231) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.004) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.617 (+/-0.238) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.642 (+/-0.236) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.681 (+/-0.295) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.658 (+/-0.318) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.586 (+/-0.235) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.611 (+/-0.240) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.588 (+/-0.233) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.588 (+/-0.231) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.589 (+/-0.231) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.004) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.642 (+/-0.236) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.642 (+/-0.236) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.664 (+/-0.316) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.657 (+/-0.316) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.586 (+/-0.235) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.611 (+/-0.240) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.588 (+/-0.233) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.588 (+/-0.231) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.589 (+/-0.231) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.004) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'linear'}
0.666 (+/-0.315) for {'C': 10.0, 'kernel': 'linear'}
0.662 (+/-0.317) for {'C': 100.0, 'kernel': 'linear'}
0.637 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
0.611 (+/-0.241) for {'C': 10000.0, 'kernel': 'linear'}
0.636 (+/-0.237) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6830128205128205
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.673 (+/-0.330) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.627 (+/-0.330) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.664 (+/-0.326) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.643 (+/-0.391) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.635 (+/-0.326) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.673 (+/-0.330) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.658 (+/-0.387) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.610 (+/-0.326) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.622 (+/-0.336) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.673 (+/-0.330) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.658 (+/-0.387) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.625 (+/-0.314) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.610 (+/-0.326) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.622 (+/-0.336) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.747 (+/-0.502) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.797 (+/-0.492) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.702 (+/-0.355) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.658 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.625 (+/-0.314) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.610 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.622 (+/-0.336) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.024) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.797 (+/-0.492) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.656 (+/-0.291) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.657 (+/-0.389) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.638 (+/-0.301) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.620 (+/-0.320) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.610 (+/-0.326) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.622 (+/-0.336) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.699 (+/-0.797) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.681 (+/-0.372) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.645 (+/-0.299) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.626 (+/-0.314) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.614 (+/-0.327) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.619 (+/-0.322) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.610 (+/-0.326) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.622 (+/-0.336) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.699 (+/-0.797) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.672 (+/-0.370) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.617 (+/-0.306) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.625 (+/-0.314) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.614 (+/-0.327) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.619 (+/-0.322) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.610 (+/-0.326) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.622 (+/-0.336) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 1.0, 'kernel': 'linear'}
0.673 (+/-0.377) for {'C': 10.0, 'kernel': 'linear'}
0.631 (+/-0.312) for {'C': 100.0, 'kernel': 'linear'}
0.630 (+/-0.310) for {'C': 1000.0, 'kernel': 'linear'}
0.619 (+/-0.321) for {'C': 10000.0, 'kernel': 'linear'}
0.627 (+/-0.313) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.006) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.666 (+/-0.315) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.611 (+/-0.242) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.004) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.315) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.609 (+/-0.243) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.240) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.004) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.666 (+/-0.315) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.658 (+/-0.314) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.587 (+/-0.233) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.589 (+/-0.231) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.004) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.315) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.658 (+/-0.314) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.636 (+/-0.236) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.588 (+/-0.232) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.589 (+/-0.231) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.004) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.617 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.642 (+/-0.236) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.682 (+/-0.294) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.658 (+/-0.313) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.636 (+/-0.235) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.588 (+/-0.233) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.588 (+/-0.231) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.589 (+/-0.231) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.004) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.523 (+/-0.138) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.642 (+/-0.236) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.681 (+/-0.292) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.657 (+/-0.315) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.662 (+/-0.224) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.612 (+/-0.241) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.588 (+/-0.233) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.588 (+/-0.231) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.589 (+/-0.231) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.004) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.642 (+/-0.236) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.641 (+/-0.235) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.677 (+/-0.294) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.657 (+/-0.317) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.586 (+/-0.235) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.611 (+/-0.240) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.588 (+/-0.233) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.588 (+/-0.231) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.589 (+/-0.231) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.004) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.642 (+/-0.236) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.641 (+/-0.234) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.658 (+/-0.315) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.657 (+/-0.316) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.586 (+/-0.235) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.611 (+/-0.240) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.588 (+/-0.233) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.588 (+/-0.231) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.589 (+/-0.231) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.004) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.663 (+/-0.316) for {'C': 10.0, 'kernel': 'linear'}
0.661 (+/-0.315) for {'C': 100.0, 'kernel': 'linear'}
0.637 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
0.611 (+/-0.241) for {'C': 10000.0, 'kernel': 'linear'}
0.636 (+/-0.237) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.17 0.17 0.17 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6818910256410255
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.496 (+/-0.001) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.747 (+/-0.502) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.747 (+/-0.502) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.747 (+/-0.502) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.747 (+/-0.502) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.747 (+/-0.502) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.747 (+/-0.502) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.747 (+/-0.502) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.747 (+/-0.502) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.772 (+/-0.473) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.748 (+/-0.449) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.747 (+/-0.502) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.747 (+/-0.502) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.747 (+/-0.502) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.747 (+/-0.502) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.747 (+/-0.502) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.747 (+/-0.502) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.747 (+/-0.502) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.747 (+/-0.502) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.797 (+/-0.492) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.747 (+/-0.449) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.731 (+/-0.422) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.747 (+/-0.502) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.747 (+/-0.502) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.797 (+/-0.492) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.747 (+/-0.502) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.747 (+/-0.502) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.747 (+/-0.502) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.747 (+/-0.502) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.797 (+/-0.492) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.748 (+/-0.449) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.731 (+/-0.422) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.723 (+/-0.423) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.747 (+/-0.502) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.797 (+/-0.492) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.797 (+/-0.492) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.747 (+/-0.502) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.797 (+/-0.492) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.797 (+/-0.492) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.722 (+/-0.473) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.748 (+/-0.449) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.731 (+/-0.422) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.698 (+/-0.383) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.698 (+/-0.383) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.747 (+/-0.502) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.797 (+/-0.492) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.797 (+/-0.492) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.747 (+/-0.502) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.798 (+/-0.492) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.798 (+/-0.492) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.747 (+/-0.449) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.731 (+/-0.422) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.698 (+/-0.383) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.698 (+/-0.383) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.706 (+/-0.383) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.797 (+/-0.492) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.797 (+/-0.492) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.798 (+/-0.492) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.797 (+/-0.492) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.748 (+/-0.449) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.798 (+/-0.437) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.722 (+/-0.417) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.685 (+/-0.384) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.698 (+/-0.383) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.664 (+/-0.326) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.723 (+/-0.417) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.797 (+/-0.492) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.798 (+/-0.492) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.798 (+/-0.492) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.798 (+/-0.492) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.748 (+/-0.449) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.773 (+/-0.416) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.664 (+/-0.388) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.664 (+/-0.326) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.664 (+/-0.317) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.656 (+/-0.312) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.714 (+/-0.418) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.797 (+/-0.492) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.798 (+/-0.492) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.773 (+/-0.474) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.798 (+/-0.492) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.735 (+/-0.455) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.702 (+/-0.363) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.689 (+/-0.375) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.706 (+/-0.383) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.648 (+/-0.315) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.646 (+/-0.316) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.648 (+/-0.316) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.797 (+/-0.492) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.798 (+/-0.492) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.760 (+/-0.482) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.714 (+/-0.418) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.708 (+/-0.401) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.685 (+/-0.352) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.698 (+/-0.376) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.652 (+/-0.313) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.635 (+/-0.310) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.653 (+/-0.333) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.632 (+/-0.310) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.797 (+/-0.492) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.798 (+/-0.492) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.806 (+/-0.473) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.702 (+/-0.421) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.684 (+/-0.353) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.662 (+/-0.289) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.689 (+/-0.375) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.652 (+/-0.313) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.633 (+/-0.312) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.668 (+/-0.382) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.629 (+/-0.311) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.797 (+/-0.492) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.798 (+/-0.492) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.753 (+/-0.491) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.685 (+/-0.384) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.666 (+/-0.354) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.676 (+/-0.294) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.689 (+/-0.375) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.645 (+/-0.314) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.632 (+/-0.313) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.668 (+/-0.383) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.627 (+/-0.313) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'linear'}
0.706 (+/-0.383) for {'C': 10.0, 'kernel': 'linear'}
0.643 (+/-0.320) for {'C': 100.0, 'kernel': 'linear'}
0.630 (+/-0.310) for {'C': 1000.0, 'kernel': 'linear'}
0.619 (+/-0.321) for {'C': 10000.0, 'kernel': 'linear'}
0.627 (+/-0.313) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.500 (+/-0.000) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.617 (+/-0.238) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.617 (+/-0.238) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.617 (+/-0.238) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.617 (+/-0.238) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.617 (+/-0.238) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.617 (+/-0.238) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.617 (+/-0.238) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.617 (+/-0.238) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.642 (+/-0.236) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.666 (+/-0.316) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.617 (+/-0.238) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.617 (+/-0.238) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.617 (+/-0.238) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.617 (+/-0.238) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.617 (+/-0.238) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.617 (+/-0.238) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.617 (+/-0.238) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.617 (+/-0.238) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.642 (+/-0.236) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.641 (+/-0.236) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.666 (+/-0.315) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.617 (+/-0.238) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.617 (+/-0.238) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.642 (+/-0.236) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.617 (+/-0.238) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.617 (+/-0.238) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.617 (+/-0.238) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.617 (+/-0.238) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.642 (+/-0.236) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.666 (+/-0.316) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.666 (+/-0.315) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.666 (+/-0.315) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.617 (+/-0.238) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.642 (+/-0.236) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.642 (+/-0.236) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.617 (+/-0.238) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.642 (+/-0.236) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.642 (+/-0.236) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.617 (+/-0.238) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.666 (+/-0.316) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.666 (+/-0.315) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.666 (+/-0.315) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.666 (+/-0.315) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.617 (+/-0.238) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.642 (+/-0.236) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.642 (+/-0.236) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.617 (+/-0.238) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.667 (+/-0.316) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.667 (+/-0.316) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.641 (+/-0.236) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.666 (+/-0.315) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.666 (+/-0.315) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.666 (+/-0.315) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.666 (+/-0.315) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.642 (+/-0.236) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.642 (+/-0.236) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.667 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.642 (+/-0.236) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.666 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.683 (+/-0.296) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.641 (+/-0.235) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.666 (+/-0.315) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.666 (+/-0.315) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.665 (+/-0.315) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.666 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.642 (+/-0.236) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.667 (+/-0.316) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.667 (+/-0.316) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.667 (+/-0.316) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.666 (+/-0.316) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.683 (+/-0.296) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.616 (+/-0.237) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.665 (+/-0.315) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.665 (+/-0.315) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.640 (+/-0.236) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.664 (+/-0.319) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.642 (+/-0.236) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.667 (+/-0.316) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.667 (+/-0.316) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.667 (+/-0.316) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.666 (+/-0.316) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.682 (+/-0.295) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.666 (+/-0.316) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.666 (+/-0.316) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.664 (+/-0.316) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.638 (+/-0.237) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.663 (+/-0.318) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.642 (+/-0.236) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.667 (+/-0.316) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.666 (+/-0.316) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.666 (+/-0.316) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.681 (+/-0.292) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.681 (+/-0.292) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.666 (+/-0.316) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.665 (+/-0.315) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.662 (+/-0.315) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.663 (+/-0.317) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.661 (+/-0.317) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.642 (+/-0.236) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.667 (+/-0.316) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.683 (+/-0.296) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.666 (+/-0.315) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.681 (+/-0.291) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.681 (+/-0.293) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.665 (+/-0.317) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.665 (+/-0.315) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.661 (+/-0.317) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.662 (+/-0.318) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.660 (+/-0.316) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.642 (+/-0.236) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.667 (+/-0.316) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.657 (+/-0.310) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.666 (+/-0.315) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.680 (+/-0.290) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.681 (+/-0.295) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.665 (+/-0.317) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.664 (+/-0.316) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.660 (+/-0.317) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.662 (+/-0.319) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.658 (+/-0.318) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'linear'}
0.666 (+/-0.315) for {'C': 10.0, 'kernel': 'linear'}
0.662 (+/-0.317) for {'C': 100.0, 'kernel': 'linear'}
0.637 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
0.611 (+/-0.241) for {'C': 10000.0, 'kernel': 'linear'}
0.636 (+/-0.237) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6830128205128205
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.747 (+/-0.502) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.747 (+/-0.502) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.797 (+/-0.492) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.797 (+/-0.492) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.797 (+/-0.492) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.797 (+/-0.492) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.797 (+/-0.492) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.797 (+/-0.492) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.748 (+/-0.449) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.685 (+/-0.384) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.702 (+/-0.355) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.747 (+/-0.502) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.797 (+/-0.492) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.797 (+/-0.492) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.797 (+/-0.492) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.798 (+/-0.492) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.798 (+/-0.492) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.798 (+/-0.492) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.798 (+/-0.437) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.689 (+/-0.374) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.714 (+/-0.359) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.723 (+/-0.358) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.747 (+/-0.502) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.797 (+/-0.492) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.797 (+/-0.492) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.797 (+/-0.492) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.798 (+/-0.492) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.798 (+/-0.492) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.748 (+/-0.449) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.739 (+/-0.398) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.651 (+/-0.308) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.664 (+/-0.317) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.681 (+/-0.372) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.747 (+/-0.502) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.797 (+/-0.492) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.798 (+/-0.492) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.797 (+/-0.492) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.823 (+/-0.451) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.798 (+/-0.437) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.677 (+/-0.374) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.697 (+/-0.353) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.664 (+/-0.317) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.681 (+/-0.387) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.669 (+/-0.373) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.747 (+/-0.502) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.798 (+/-0.492) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.848 (+/-0.460) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.798 (+/-0.492) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.764 (+/-0.421) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.739 (+/-0.392) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.660 (+/-0.327) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.651 (+/-0.308) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.673 (+/-0.370) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.666 (+/-0.376) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.660 (+/-0.385) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.797 (+/-0.492) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.798 (+/-0.492) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.773 (+/-0.474) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.714 (+/-0.418) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.691 (+/-0.356) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.656 (+/-0.291) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.659 (+/-0.313) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.631 (+/-0.298) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.654 (+/-0.379) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.630 (+/-0.314) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.657 (+/-0.389) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.797 (+/-0.492) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.798 (+/-0.492) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.735 (+/-0.455) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.674 (+/-0.376) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.654 (+/-0.370) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.666 (+/-0.295) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.647 (+/-0.308) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.626 (+/-0.302) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.626 (+/-0.314) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.624 (+/-0.316) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.656 (+/-0.389) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.772 (+/-0.473) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.798 (+/-0.492) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.721 (+/-0.463) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.673 (+/-0.391) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.638 (+/-0.299) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.646 (+/-0.285) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.639 (+/-0.314) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.665 (+/-0.382) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.623 (+/-0.317) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.623 (+/-0.317) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.625 (+/-0.315) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.764 (+/-0.478) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.773 (+/-0.474) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.746 (+/-0.451) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.676 (+/-0.433) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.639 (+/-0.289) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.649 (+/-0.285) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.633 (+/-0.323) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.620 (+/-0.315) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.622 (+/-0.318) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.622 (+/-0.317) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.626 (+/-0.314) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.689 (+/-0.374) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.748 (+/-0.449) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.663 (+/-0.390) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.652 (+/-0.381) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.647 (+/-0.288) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.639 (+/-0.289) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.622 (+/-0.318) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.619 (+/-0.316) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.622 (+/-0.317) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.623 (+/-0.316) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.628 (+/-0.312) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.681 (+/-0.372) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.748 (+/-0.449) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.656 (+/-0.393) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.648 (+/-0.382) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.630 (+/-0.279) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.645 (+/-0.299) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.622 (+/-0.318) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.618 (+/-0.317) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.622 (+/-0.318) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.623 (+/-0.317) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.626 (+/-0.314) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 1.0, 'kernel': 'linear'}
0.673 (+/-0.377) for {'C': 10.0, 'kernel': 'linear'}
0.631 (+/-0.312) for {'C': 100.0, 'kernel': 'linear'}
0.630 (+/-0.310) for {'C': 1000.0, 'kernel': 'linear'}
0.619 (+/-0.321) for {'C': 10000.0, 'kernel': 'linear'}
0.627 (+/-0.313) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.617 (+/-0.238) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.617 (+/-0.238) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.642 (+/-0.236) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.642 (+/-0.236) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.642 (+/-0.236) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.642 (+/-0.236) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.642 (+/-0.236) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.642 (+/-0.236) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.666 (+/-0.316) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.666 (+/-0.315) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.682 (+/-0.294) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.617 (+/-0.238) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.642 (+/-0.236) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.642 (+/-0.236) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.642 (+/-0.236) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.667 (+/-0.316) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.667 (+/-0.316) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.667 (+/-0.316) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.683 (+/-0.296) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.666 (+/-0.314) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.682 (+/-0.295) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.682 (+/-0.296) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.617 (+/-0.238) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.642 (+/-0.236) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.642 (+/-0.236) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.642 (+/-0.236) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.667 (+/-0.316) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.667 (+/-0.316) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.666 (+/-0.316) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.682 (+/-0.295) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.665 (+/-0.313) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.665 (+/-0.315) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.665 (+/-0.315) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.617 (+/-0.238) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.642 (+/-0.236) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.667 (+/-0.316) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.642 (+/-0.236) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.683 (+/-0.296) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.683 (+/-0.296) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.665 (+/-0.314) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.682 (+/-0.294) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.665 (+/-0.315) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.665 (+/-0.316) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.664 (+/-0.316) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.617 (+/-0.238) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.667 (+/-0.316) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.683 (+/-0.296) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.667 (+/-0.316) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.683 (+/-0.296) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.683 (+/-0.296) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.665 (+/-0.315) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.665 (+/-0.314) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.665 (+/-0.314) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.663 (+/-0.316) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.661 (+/-0.313) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.642 (+/-0.236) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.667 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.667 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.666 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.682 (+/-0.294) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.681 (+/-0.292) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.665 (+/-0.314) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.664 (+/-0.314) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.662 (+/-0.312) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.660 (+/-0.315) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.657 (+/-0.315) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.642 (+/-0.236) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.667 (+/-0.316) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.666 (+/-0.316) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.665 (+/-0.314) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.679 (+/-0.288) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.681 (+/-0.293) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.664 (+/-0.314) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.662 (+/-0.316) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.659 (+/-0.313) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.656 (+/-0.316) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.656 (+/-0.315) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.642 (+/-0.236) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.667 (+/-0.316) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.665 (+/-0.315) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.664 (+/-0.314) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.679 (+/-0.287) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.680 (+/-0.292) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.662 (+/-0.317) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.660 (+/-0.319) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.655 (+/-0.314) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.655 (+/-0.316) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.658 (+/-0.315) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.641 (+/-0.236) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.667 (+/-0.316) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.681 (+/-0.294) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.663 (+/-0.314) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.678 (+/-0.289) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.679 (+/-0.294) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.659 (+/-0.312) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.656 (+/-0.316) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.654 (+/-0.315) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.654 (+/-0.316) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.658 (+/-0.316) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.641 (+/-0.235) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.666 (+/-0.316) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.656 (+/-0.308) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.663 (+/-0.315) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.678 (+/-0.291) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.678 (+/-0.293) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.657 (+/-0.309) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.654 (+/-0.316) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.655 (+/-0.315) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.655 (+/-0.316) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.659 (+/-0.316) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.641 (+/-0.235) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.666 (+/-0.316) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.655 (+/-0.308) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.662 (+/-0.315) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.677 (+/-0.294) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.677 (+/-0.294) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.655 (+/-0.310) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.653 (+/-0.316) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.655 (+/-0.313) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.656 (+/-0.314) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.657 (+/-0.317) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.663 (+/-0.316) for {'C': 10.0, 'kernel': 'linear'}
0.661 (+/-0.315) for {'C': 100.0, 'kernel': 'linear'}
0.637 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
0.611 (+/-0.241) for {'C': 10000.0, 'kernel': 'linear'}
0.636 (+/-0.237) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6833333333333332
循环迭代之前,delta is: [3.69042656e+05 7.48811357e-07]
执行tunemodel()函数前,使用的grid search parameter是:
[{'C': array([ 398107.1705535 , 436515.83224017, 478630.09232264,
524807.46024977, 575439.93733716, 630957.34448019,
691830.97091894, 758577.57502918, 831763.77110267,
912010.83935591, 1000000. ]), 'kernel': ['rbf'], 'gamma': array([1.58489319e-07, 1.73780083e-07, 1.90546072e-07, 2.08929613e-07,
2.29086765e-07, 2.51188643e-07, 2.75422870e-07, 3.01995172e-07,
3.31131121e-07, 3.63078055e-07, 3.98107171e-07])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.797 (+/-0.492) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.797 (+/-0.492) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.747 (+/-0.502) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.797 (+/-0.492) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.797 (+/-0.492) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.797 (+/-0.492) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.797 (+/-0.492) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.797 (+/-0.492) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.747 (+/-0.502) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.747 (+/-0.502) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.747 (+/-0.502) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.797 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.797 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.747 (+/-0.502) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.797 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.797 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.797 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.797 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.797 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.747 (+/-0.502) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.747 (+/-0.502) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.747 (+/-0.502) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.797 (+/-0.492) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.797 (+/-0.492) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.797 (+/-0.492) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.797 (+/-0.492) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.797 (+/-0.492) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.797 (+/-0.492) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.797 (+/-0.492) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.797 (+/-0.492) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.747 (+/-0.502) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.747 (+/-0.502) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.797 (+/-0.492) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.797 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.797 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.747 (+/-0.502) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.798 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.797 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.797 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.797 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.797 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.747 (+/-0.502) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.747 (+/-0.502) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.797 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.797 (+/-0.492) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.798 (+/-0.492) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.747 (+/-0.502) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.798 (+/-0.492) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.797 (+/-0.492) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.797 (+/-0.492) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.797 (+/-0.492) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.797 (+/-0.492) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.747 (+/-0.502) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.747 (+/-0.502) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.797 (+/-0.492) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.797 (+/-0.492) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.798 (+/-0.492) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.747 (+/-0.502) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.798 (+/-0.492) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.797 (+/-0.492) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.797 (+/-0.492) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.797 (+/-0.492) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.797 (+/-0.492) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.747 (+/-0.502) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.747 (+/-0.502) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.747 (+/-0.502) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.797 (+/-0.492) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.798 (+/-0.492) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.747 (+/-0.502) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.798 (+/-0.492) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.797 (+/-0.492) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.798 (+/-0.492) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.797 (+/-0.492) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.797 (+/-0.492) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.747 (+/-0.502) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.747 (+/-0.502) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.747 (+/-0.502) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.797 (+/-0.492) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.798 (+/-0.492) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.747 (+/-0.502) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.798 (+/-0.492) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.797 (+/-0.492) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.798 (+/-0.492) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.798 (+/-0.492) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.797 (+/-0.492) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.747 (+/-0.502) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.747 (+/-0.502) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.797 (+/-0.492) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.797 (+/-0.492) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.798 (+/-0.492) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.797 (+/-0.492) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.798 (+/-0.492) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.797 (+/-0.492) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.798 (+/-0.492) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.797 (+/-0.492) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.797 (+/-0.492) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.747 (+/-0.502) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.747 (+/-0.502) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.797 (+/-0.492) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.797 (+/-0.492) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.798 (+/-0.492) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.797 (+/-0.492) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.798 (+/-0.492) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.797 (+/-0.492) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.798 (+/-0.492) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.797 (+/-0.492) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.797 (+/-0.492) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.747 (+/-0.502) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.747 (+/-0.502) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.797 (+/-0.492) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.797 (+/-0.492) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.798 (+/-0.492) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.797 (+/-0.492) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.798 (+/-0.492) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.797 (+/-0.492) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.798 (+/-0.492) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.798 (+/-0.492) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.797 (+/-0.492) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.747 (+/-0.502) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.747 (+/-0.502) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.797 (+/-0.492) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'linear'}
0.706 (+/-0.383) for {'C': 10.0, 'kernel': 'linear'}
0.643 (+/-0.320) for {'C': 100.0, 'kernel': 'linear'}
0.630 (+/-0.310) for {'C': 1000.0, 'kernel': 'linear'}
0.619 (+/-0.321) for {'C': 10000.0, 'kernel': 'linear'}
0.627 (+/-0.313) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.642 (+/-0.236) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.642 (+/-0.236) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.617 (+/-0.238) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.642 (+/-0.236) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.642 (+/-0.236) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.642 (+/-0.236) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.642 (+/-0.236) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.642 (+/-0.236) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.617 (+/-0.238) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.617 (+/-0.238) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.617 (+/-0.238) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.642 (+/-0.236) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.642 (+/-0.236) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.617 (+/-0.238) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.642 (+/-0.236) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.642 (+/-0.236) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.642 (+/-0.236) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.642 (+/-0.236) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.642 (+/-0.236) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.617 (+/-0.238) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.617 (+/-0.238) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.617 (+/-0.238) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.642 (+/-0.236) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.642 (+/-0.236) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.642 (+/-0.236) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.642 (+/-0.236) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.642 (+/-0.236) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.642 (+/-0.236) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.642 (+/-0.236) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.642 (+/-0.236) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.617 (+/-0.238) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.617 (+/-0.238) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.642 (+/-0.236) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.642 (+/-0.236) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.642 (+/-0.236) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.617 (+/-0.238) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.667 (+/-0.316) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.642 (+/-0.236) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.642 (+/-0.236) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.642 (+/-0.236) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.642 (+/-0.236) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.617 (+/-0.238) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.617 (+/-0.238) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.642 (+/-0.236) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.642 (+/-0.236) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.667 (+/-0.316) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.617 (+/-0.238) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.667 (+/-0.316) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.642 (+/-0.236) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.642 (+/-0.236) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.642 (+/-0.236) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.642 (+/-0.236) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.617 (+/-0.238) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.617 (+/-0.238) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.642 (+/-0.236) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.642 (+/-0.236) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.667 (+/-0.316) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.617 (+/-0.238) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.667 (+/-0.316) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.642 (+/-0.236) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.642 (+/-0.236) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.642 (+/-0.236) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.642 (+/-0.236) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.617 (+/-0.238) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.617 (+/-0.238) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.617 (+/-0.238) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.642 (+/-0.236) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.667 (+/-0.316) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.617 (+/-0.238) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.667 (+/-0.316) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.642 (+/-0.236) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.667 (+/-0.316) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.642 (+/-0.236) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.642 (+/-0.236) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.617 (+/-0.238) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.617 (+/-0.238) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.617 (+/-0.238) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.642 (+/-0.236) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.667 (+/-0.316) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.617 (+/-0.238) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.667 (+/-0.316) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.642 (+/-0.236) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.667 (+/-0.316) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.667 (+/-0.316) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.642 (+/-0.236) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.617 (+/-0.238) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.617 (+/-0.238) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.642 (+/-0.236) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.642 (+/-0.236) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.667 (+/-0.316) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.642 (+/-0.236) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.667 (+/-0.316) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.642 (+/-0.236) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.667 (+/-0.316) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.642 (+/-0.236) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.642 (+/-0.236) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.617 (+/-0.238) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.617 (+/-0.238) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.642 (+/-0.236) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.642 (+/-0.236) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.667 (+/-0.316) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.642 (+/-0.236) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.667 (+/-0.316) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.642 (+/-0.236) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.667 (+/-0.316) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.642 (+/-0.236) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.642 (+/-0.236) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.617 (+/-0.238) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.617 (+/-0.238) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.642 (+/-0.236) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.642 (+/-0.236) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.667 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.642 (+/-0.236) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.667 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.642 (+/-0.236) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.667 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.667 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.642 (+/-0.236) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.617 (+/-0.238) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.617 (+/-0.238) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.642 (+/-0.236) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'linear'}
0.666 (+/-0.315) for {'C': 10.0, 'kernel': 'linear'}
0.662 (+/-0.317) for {'C': 100.0, 'kernel': 'linear'}
0.637 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
0.611 (+/-0.241) for {'C': 10000.0, 'kernel': 'linear'}
0.636 (+/-0.237) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6666666666666665
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.797 (+/-0.492) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.797 (+/-0.492) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.797 (+/-0.492) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.798 (+/-0.492) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.798 (+/-0.492) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.798 (+/-0.492) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.798 (+/-0.492) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.798 (+/-0.492) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.797 (+/-0.492) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.797 (+/-0.492) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.797 (+/-0.492) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.798 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.798 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.798 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.798 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.798 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.798 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.798 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.798 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.797 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.798 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.798 (+/-0.492) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.797 (+/-0.492) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.798 (+/-0.492) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.798 (+/-0.492) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.798 (+/-0.492) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.798 (+/-0.492) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.798 (+/-0.492) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.798 (+/-0.492) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.798 (+/-0.492) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.797 (+/-0.492) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.798 (+/-0.492) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.798 (+/-0.492) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.798 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.798 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.798 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.798 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.798 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.848 (+/-0.460) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.798 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.798 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.797 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.798 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.798 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.798 (+/-0.492) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.798 (+/-0.492) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.798 (+/-0.492) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.798 (+/-0.492) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.798 (+/-0.492) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.848 (+/-0.460) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.798 (+/-0.492) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.798 (+/-0.492) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.797 (+/-0.492) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.798 (+/-0.492) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.798 (+/-0.492) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.798 (+/-0.492) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.798 (+/-0.492) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.798 (+/-0.492) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.798 (+/-0.492) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.773 (+/-0.474) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.848 (+/-0.460) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.798 (+/-0.492) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.798 (+/-0.492) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.797 (+/-0.492) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.798 (+/-0.492) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.798 (+/-0.492) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.798 (+/-0.492) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.798 (+/-0.492) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.798 (+/-0.492) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.798 (+/-0.492) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.773 (+/-0.474) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.798 (+/-0.492) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.798 (+/-0.492) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.798 (+/-0.492) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.798 (+/-0.492) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.798 (+/-0.492) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.798 (+/-0.492) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.798 (+/-0.492) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.798 (+/-0.492) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.798 (+/-0.492) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.798 (+/-0.492) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.773 (+/-0.474) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.773 (+/-0.474) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.848 (+/-0.460) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.798 (+/-0.492) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.798 (+/-0.492) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.798 (+/-0.492) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.798 (+/-0.492) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.798 (+/-0.492) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.798 (+/-0.492) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.798 (+/-0.492) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.798 (+/-0.492) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.773 (+/-0.474) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.773 (+/-0.474) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.848 (+/-0.460) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.798 (+/-0.492) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.798 (+/-0.492) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.798 (+/-0.492) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.798 (+/-0.492) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.798 (+/-0.492) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.798 (+/-0.492) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.798 (+/-0.492) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.798 (+/-0.492) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.773 (+/-0.474) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.773 (+/-0.474) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.848 (+/-0.460) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.848 (+/-0.460) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.798 (+/-0.492) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.798 (+/-0.492) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.773 (+/-0.474) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.798 (+/-0.492) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.798 (+/-0.492) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.798 (+/-0.492) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.798 (+/-0.492) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.764 (+/-0.478) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.773 (+/-0.474) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.848 (+/-0.460) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.848 (+/-0.460) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.798 (+/-0.492) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.798 (+/-0.492) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.714 (+/-0.418) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 1.0, 'kernel': 'linear'}
0.673 (+/-0.377) for {'C': 10.0, 'kernel': 'linear'}
0.631 (+/-0.312) for {'C': 100.0, 'kernel': 'linear'}
0.630 (+/-0.310) for {'C': 1000.0, 'kernel': 'linear'}
0.619 (+/-0.321) for {'C': 10000.0, 'kernel': 'linear'}
0.627 (+/-0.313) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.642 (+/-0.236) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.642 (+/-0.236) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.642 (+/-0.236) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.667 (+/-0.316) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.667 (+/-0.316) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.667 (+/-0.316) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.667 (+/-0.316) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.667 (+/-0.316) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.642 (+/-0.236) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.642 (+/-0.236) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.642 (+/-0.236) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.667 (+/-0.316) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.667 (+/-0.316) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.667 (+/-0.316) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.667 (+/-0.316) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.667 (+/-0.316) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.667 (+/-0.316) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.667 (+/-0.316) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.667 (+/-0.316) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.642 (+/-0.236) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.667 (+/-0.316) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.667 (+/-0.316) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.642 (+/-0.236) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.667 (+/-0.316) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.667 (+/-0.316) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.667 (+/-0.316) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.667 (+/-0.316) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.667 (+/-0.316) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.667 (+/-0.316) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.667 (+/-0.316) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.642 (+/-0.236) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.667 (+/-0.316) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.667 (+/-0.316) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.667 (+/-0.316) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.667 (+/-0.316) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.667 (+/-0.316) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.667 (+/-0.316) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.667 (+/-0.316) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.683 (+/-0.296) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.667 (+/-0.316) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.667 (+/-0.316) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.642 (+/-0.236) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.667 (+/-0.316) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.667 (+/-0.316) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.667 (+/-0.316) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.667 (+/-0.316) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.667 (+/-0.316) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.667 (+/-0.316) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.667 (+/-0.316) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.683 (+/-0.296) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.667 (+/-0.316) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.667 (+/-0.316) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.642 (+/-0.236) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.667 (+/-0.316) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.667 (+/-0.316) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.667 (+/-0.316) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.667 (+/-0.316) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.667 (+/-0.316) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.667 (+/-0.316) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.667 (+/-0.316) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.683 (+/-0.296) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.667 (+/-0.316) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.667 (+/-0.316) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.642 (+/-0.236) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.667 (+/-0.316) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.667 (+/-0.316) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.667 (+/-0.316) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.667 (+/-0.316) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.667 (+/-0.316) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.667 (+/-0.316) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.667 (+/-0.316) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.667 (+/-0.316) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.667 (+/-0.316) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.667 (+/-0.316) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.667 (+/-0.316) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.667 (+/-0.316) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.667 (+/-0.316) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.667 (+/-0.316) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.667 (+/-0.316) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.667 (+/-0.316) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.667 (+/-0.316) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.667 (+/-0.316) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.667 (+/-0.316) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.683 (+/-0.296) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.667 (+/-0.316) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.667 (+/-0.316) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.667 (+/-0.316) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.667 (+/-0.316) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.667 (+/-0.316) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.667 (+/-0.316) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.667 (+/-0.316) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.667 (+/-0.316) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.667 (+/-0.316) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.667 (+/-0.316) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.683 (+/-0.296) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.667 (+/-0.316) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.667 (+/-0.316) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.667 (+/-0.316) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.667 (+/-0.316) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.667 (+/-0.316) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.667 (+/-0.316) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.667 (+/-0.316) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.667 (+/-0.316) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.667 (+/-0.316) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.667 (+/-0.316) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.683 (+/-0.296) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.683 (+/-0.296) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.667 (+/-0.316) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.667 (+/-0.316) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.667 (+/-0.316) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.667 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.667 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.667 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.667 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.666 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.667 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.683 (+/-0.296) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.683 (+/-0.296) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.667 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.667 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.666 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.663 (+/-0.316) for {'C': 10.0, 'kernel': 'linear'}
0.661 (+/-0.315) for {'C': 100.0, 'kernel': 'linear'}
0.637 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
0.611 (+/-0.241) for {'C': 10000.0, 'kernel': 'linear'}
0.636 (+/-0.237) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6833333333333332
第0轮loop中,tunemodel函数得到的最优参数是: ([{'C': array([478630.09232264, 487528.49010339, 496592.32145034, 505824.66200311,
515228.64458176, 524807.46024977, 534564.35939697, 544502.65284242,
554625.71295791, 564936.9748123 , 575439.93733716]), 'kernel': ['rbf'], 'gamma': array([2.29086765e-07, 2.33345806e-07, 2.37684029e-07, 2.42102905e-07,
2.46603934e-07, 2.51188643e-07, 2.55858589e-07, 2.60615355e-07,
2.65460556e-07, 2.70395836e-07, 2.75422870e-07])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}], {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}, 0.6833333333333332)
这是第0次迭代微调C和gamma。
第0次迭代,得到delta: [1.06149884e+05 6.35274710e-22]
执行tunemodel()函数前,使用的grid search parameter是:
[{'C': array([478630.09232264, 487528.49010339, 496592.32145034, 505824.66200311,
515228.64458176, 524807.46024977, 534564.35939697, 544502.65284242,
554625.71295791, 564936.9748123 , 575439.93733716]), 'kernel': ['rbf'], 'gamma': array([2.29086765e-07, 2.33345806e-07, 2.37684029e-07, 2.42102905e-07,
2.46603934e-07, 2.51188643e-07, 2.55858589e-07, 2.60615355e-07,
2.65460556e-07, 2.70395836e-07, 2.75422870e-07])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.797 (+/-0.492) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.747 (+/-0.502) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.797 (+/-0.492) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.797 (+/-0.492) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.797 (+/-0.492) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.797 (+/-0.492) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.797 (+/-0.492) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.797 (+/-0.492) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.797 (+/-0.492) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.797 (+/-0.492) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.797 (+/-0.492) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.797 (+/-0.492) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.747 (+/-0.502) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.797 (+/-0.492) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.797 (+/-0.492) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.797 (+/-0.492) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.797 (+/-0.492) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.797 (+/-0.492) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.797 (+/-0.492) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.797 (+/-0.492) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.797 (+/-0.492) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.797 (+/-0.492) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.797 (+/-0.492) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.747 (+/-0.502) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.797 (+/-0.492) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.798 (+/-0.492) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.797 (+/-0.492) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.797 (+/-0.492) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.797 (+/-0.492) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.797 (+/-0.492) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.797 (+/-0.492) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.797 (+/-0.492) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.797 (+/-0.492) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.797 (+/-0.492) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.747 (+/-0.502) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.797 (+/-0.492) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.798 (+/-0.492) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.797 (+/-0.492) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.797 (+/-0.492) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.797 (+/-0.492) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.797 (+/-0.492) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.797 (+/-0.492) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.797 (+/-0.492) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.797 (+/-0.492) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.797 (+/-0.492) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.747 (+/-0.502) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.797 (+/-0.492) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.798 (+/-0.492) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.797 (+/-0.492) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.797 (+/-0.492) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.797 (+/-0.492) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.797 (+/-0.492) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.797 (+/-0.492) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.797 (+/-0.492) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.797 (+/-0.492) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.797 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.747 (+/-0.502) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.797 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.797 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.797 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.797 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.797 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.797 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.797 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.797 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.797 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.797 (+/-0.492) for {'C': 534564.3593969719, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.747 (+/-0.502) for {'C': 534564.3593969719, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.797 (+/-0.492) for {'C': 534564.3593969719, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.798 (+/-0.492) for {'C': 534564.3593969719, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.797 (+/-0.492) for {'C': 534564.3593969719, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.797 (+/-0.492) for {'C': 534564.3593969719, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.797 (+/-0.492) for {'C': 534564.3593969719, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.797 (+/-0.492) for {'C': 534564.3593969719, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.797 (+/-0.492) for {'C': 534564.3593969719, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.797 (+/-0.492) for {'C': 534564.3593969719, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.797 (+/-0.492) for {'C': 534564.3593969719, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.797 (+/-0.492) for {'C': 544502.6528424212, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.747 (+/-0.502) for {'C': 544502.6528424212, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.797 (+/-0.492) for {'C': 544502.6528424212, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.798 (+/-0.492) for {'C': 544502.6528424212, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.797 (+/-0.492) for {'C': 544502.6528424212, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.797 (+/-0.492) for {'C': 544502.6528424212, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.797 (+/-0.492) for {'C': 544502.6528424212, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.797 (+/-0.492) for {'C': 544502.6528424212, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.797 (+/-0.492) for {'C': 544502.6528424212, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.797 (+/-0.492) for {'C': 544502.6528424212, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.797 (+/-0.492) for {'C': 544502.6528424212, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.797 (+/-0.492) for {'C': 554625.7129579106, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.797 (+/-0.492) for {'C': 554625.7129579106, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.797 (+/-0.492) for {'C': 554625.7129579106, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.798 (+/-0.492) for {'C': 554625.7129579106, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.797 (+/-0.492) for {'C': 554625.7129579106, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.797 (+/-0.492) for {'C': 554625.7129579106, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.797 (+/-0.492) for {'C': 554625.7129579106, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.797 (+/-0.492) for {'C': 554625.7129579106, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.797 (+/-0.492) for {'C': 554625.7129579106, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.797 (+/-0.492) for {'C': 554625.7129579106, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.797 (+/-0.492) for {'C': 554625.7129579106, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.797 (+/-0.492) for {'C': 564936.9748123022, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.797 (+/-0.492) for {'C': 564936.9748123022, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.797 (+/-0.492) for {'C': 564936.9748123022, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.798 (+/-0.492) for {'C': 564936.9748123022, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.797 (+/-0.492) for {'C': 564936.9748123022, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.797 (+/-0.492) for {'C': 564936.9748123022, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.797 (+/-0.492) for {'C': 564936.9748123022, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.797 (+/-0.492) for {'C': 564936.9748123022, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.797 (+/-0.492) for {'C': 564936.9748123022, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.797 (+/-0.492) for {'C': 564936.9748123022, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.797 (+/-0.492) for {'C': 564936.9748123022, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.797 (+/-0.492) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.747 (+/-0.502) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.797 (+/-0.492) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.798 (+/-0.492) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.797 (+/-0.492) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.797 (+/-0.492) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.797 (+/-0.492) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.797 (+/-0.492) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.797 (+/-0.492) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.797 (+/-0.492) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.797 (+/-0.492) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'linear'}
0.706 (+/-0.383) for {'C': 10.0, 'kernel': 'linear'}
0.643 (+/-0.320) for {'C': 100.0, 'kernel': 'linear'}
0.630 (+/-0.310) for {'C': 1000.0, 'kernel': 'linear'}
0.619 (+/-0.321) for {'C': 10000.0, 'kernel': 'linear'}
0.627 (+/-0.313) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.642 (+/-0.236) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.617 (+/-0.238) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.642 (+/-0.236) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.642 (+/-0.236) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.642 (+/-0.236) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.642 (+/-0.236) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.642 (+/-0.236) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.642 (+/-0.236) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.642 (+/-0.236) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.642 (+/-0.236) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.642 (+/-0.236) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.642 (+/-0.236) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.617 (+/-0.238) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.642 (+/-0.236) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.642 (+/-0.236) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.642 (+/-0.236) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.642 (+/-0.236) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.642 (+/-0.236) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.642 (+/-0.236) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.642 (+/-0.236) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.642 (+/-0.236) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.642 (+/-0.236) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.642 (+/-0.236) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.617 (+/-0.238) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.642 (+/-0.236) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.667 (+/-0.316) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.642 (+/-0.236) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.642 (+/-0.236) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.642 (+/-0.236) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.642 (+/-0.236) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.642 (+/-0.236) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.642 (+/-0.236) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.642 (+/-0.236) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.642 (+/-0.236) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.617 (+/-0.238) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.642 (+/-0.236) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.667 (+/-0.316) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.642 (+/-0.236) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.642 (+/-0.236) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.642 (+/-0.236) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.642 (+/-0.236) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.642 (+/-0.236) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.642 (+/-0.236) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.642 (+/-0.236) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.642 (+/-0.236) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.617 (+/-0.238) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.642 (+/-0.236) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.667 (+/-0.316) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.642 (+/-0.236) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.642 (+/-0.236) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.642 (+/-0.236) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.642 (+/-0.236) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.642 (+/-0.236) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.642 (+/-0.236) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.642 (+/-0.236) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.642 (+/-0.236) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.617 (+/-0.238) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.642 (+/-0.236) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.642 (+/-0.236) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.642 (+/-0.236) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.642 (+/-0.236) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.642 (+/-0.236) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.642 (+/-0.236) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.642 (+/-0.236) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.642 (+/-0.236) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.642 (+/-0.236) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.642 (+/-0.236) for {'C': 534564.3593969719, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.617 (+/-0.238) for {'C': 534564.3593969719, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.642 (+/-0.236) for {'C': 534564.3593969719, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.667 (+/-0.316) for {'C': 534564.3593969719, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.642 (+/-0.236) for {'C': 534564.3593969719, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.642 (+/-0.236) for {'C': 534564.3593969719, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.642 (+/-0.236) for {'C': 534564.3593969719, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.642 (+/-0.236) for {'C': 534564.3593969719, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.642 (+/-0.236) for {'C': 534564.3593969719, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.642 (+/-0.236) for {'C': 534564.3593969719, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.642 (+/-0.236) for {'C': 534564.3593969719, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.642 (+/-0.236) for {'C': 544502.6528424212, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.617 (+/-0.238) for {'C': 544502.6528424212, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.642 (+/-0.236) for {'C': 544502.6528424212, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.667 (+/-0.316) for {'C': 544502.6528424212, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.642 (+/-0.236) for {'C': 544502.6528424212, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.642 (+/-0.236) for {'C': 544502.6528424212, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.642 (+/-0.236) for {'C': 544502.6528424212, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.642 (+/-0.236) for {'C': 544502.6528424212, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.642 (+/-0.236) for {'C': 544502.6528424212, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.642 (+/-0.236) for {'C': 544502.6528424212, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.642 (+/-0.236) for {'C': 544502.6528424212, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.642 (+/-0.236) for {'C': 554625.7129579106, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.642 (+/-0.236) for {'C': 554625.7129579106, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.642 (+/-0.236) for {'C': 554625.7129579106, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.667 (+/-0.316) for {'C': 554625.7129579106, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.642 (+/-0.236) for {'C': 554625.7129579106, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.642 (+/-0.236) for {'C': 554625.7129579106, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.642 (+/-0.236) for {'C': 554625.7129579106, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.642 (+/-0.236) for {'C': 554625.7129579106, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.642 (+/-0.236) for {'C': 554625.7129579106, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.642 (+/-0.236) for {'C': 554625.7129579106, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.642 (+/-0.236) for {'C': 554625.7129579106, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.642 (+/-0.236) for {'C': 564936.9748123022, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.642 (+/-0.236) for {'C': 564936.9748123022, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.642 (+/-0.236) for {'C': 564936.9748123022, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.667 (+/-0.316) for {'C': 564936.9748123022, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.642 (+/-0.236) for {'C': 564936.9748123022, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.642 (+/-0.236) for {'C': 564936.9748123022, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.642 (+/-0.236) for {'C': 564936.9748123022, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.642 (+/-0.236) for {'C': 564936.9748123022, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.642 (+/-0.236) for {'C': 564936.9748123022, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.642 (+/-0.236) for {'C': 564936.9748123022, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.642 (+/-0.236) for {'C': 564936.9748123022, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.642 (+/-0.236) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.617 (+/-0.238) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.642 (+/-0.236) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.667 (+/-0.316) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.642 (+/-0.236) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.642 (+/-0.236) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.642 (+/-0.236) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.642 (+/-0.236) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.642 (+/-0.236) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.642 (+/-0.236) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.642 (+/-0.236) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'linear'}
0.666 (+/-0.315) for {'C': 10.0, 'kernel': 'linear'}
0.662 (+/-0.317) for {'C': 100.0, 'kernel': 'linear'}
0.637 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
0.611 (+/-0.241) for {'C': 10000.0, 'kernel': 'linear'}
0.636 (+/-0.237) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6666666666666665
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.798 (+/-0.492) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.797 (+/-0.492) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.798 (+/-0.492) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.798 (+/-0.492) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.798 (+/-0.492) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.798 (+/-0.492) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.848 (+/-0.460) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.798 (+/-0.492) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.798 (+/-0.492) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.798 (+/-0.492) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.798 (+/-0.492) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.798 (+/-0.492) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.797 (+/-0.492) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.798 (+/-0.492) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.798 (+/-0.492) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.798 (+/-0.492) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.798 (+/-0.492) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.798 (+/-0.492) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.798 (+/-0.492) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.798 (+/-0.492) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.798 (+/-0.492) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.798 (+/-0.492) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.798 (+/-0.492) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.797 (+/-0.492) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.798 (+/-0.492) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.798 (+/-0.492) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.798 (+/-0.492) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.798 (+/-0.492) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.798 (+/-0.492) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.798 (+/-0.492) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.798 (+/-0.492) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.798 (+/-0.492) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.798 (+/-0.492) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.798 (+/-0.492) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.797 (+/-0.492) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.798 (+/-0.492) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.798 (+/-0.492) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.798 (+/-0.492) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.798 (+/-0.492) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.798 (+/-0.492) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.798 (+/-0.492) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.798 (+/-0.492) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.798 (+/-0.492) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.798 (+/-0.492) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.798 (+/-0.492) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.797 (+/-0.492) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.798 (+/-0.492) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.798 (+/-0.492) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.798 (+/-0.492) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.798 (+/-0.492) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.798 (+/-0.492) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.798 (+/-0.492) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.798 (+/-0.492) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.798 (+/-0.492) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.798 (+/-0.492) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.798 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.797 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.798 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.798 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.798 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.848 (+/-0.460) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.798 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.798 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.798 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.798 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.798 (+/-0.492) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.798 (+/-0.492) for {'C': 534564.3593969719, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.797 (+/-0.492) for {'C': 534564.3593969719, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.798 (+/-0.492) for {'C': 534564.3593969719, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.798 (+/-0.492) for {'C': 534564.3593969719, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.798 (+/-0.492) for {'C': 534564.3593969719, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.848 (+/-0.460) for {'C': 534564.3593969719, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.798 (+/-0.492) for {'C': 534564.3593969719, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.798 (+/-0.492) for {'C': 534564.3593969719, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.798 (+/-0.492) for {'C': 534564.3593969719, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.798 (+/-0.492) for {'C': 534564.3593969719, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.798 (+/-0.492) for {'C': 534564.3593969719, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.798 (+/-0.492) for {'C': 544502.6528424212, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.797 (+/-0.492) for {'C': 544502.6528424212, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.798 (+/-0.492) for {'C': 544502.6528424212, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.798 (+/-0.492) for {'C': 544502.6528424212, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.798 (+/-0.492) for {'C': 544502.6528424212, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.848 (+/-0.460) for {'C': 544502.6528424212, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.798 (+/-0.492) for {'C': 544502.6528424212, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.798 (+/-0.492) for {'C': 544502.6528424212, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.798 (+/-0.492) for {'C': 544502.6528424212, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.798 (+/-0.492) for {'C': 544502.6528424212, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.798 (+/-0.492) for {'C': 544502.6528424212, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.798 (+/-0.492) for {'C': 554625.7129579106, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.797 (+/-0.492) for {'C': 554625.7129579106, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.798 (+/-0.492) for {'C': 554625.7129579106, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.798 (+/-0.492) for {'C': 554625.7129579106, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.798 (+/-0.492) for {'C': 554625.7129579106, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.848 (+/-0.460) for {'C': 554625.7129579106, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.798 (+/-0.492) for {'C': 554625.7129579106, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.798 (+/-0.492) for {'C': 554625.7129579106, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.798 (+/-0.492) for {'C': 554625.7129579106, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.798 (+/-0.492) for {'C': 554625.7129579106, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.798 (+/-0.492) for {'C': 554625.7129579106, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.798 (+/-0.492) for {'C': 564936.9748123022, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.797 (+/-0.492) for {'C': 564936.9748123022, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.798 (+/-0.492) for {'C': 564936.9748123022, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.798 (+/-0.492) for {'C': 564936.9748123022, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.798 (+/-0.492) for {'C': 564936.9748123022, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.848 (+/-0.460) for {'C': 564936.9748123022, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.798 (+/-0.492) for {'C': 564936.9748123022, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.798 (+/-0.492) for {'C': 564936.9748123022, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.798 (+/-0.492) for {'C': 564936.9748123022, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.798 (+/-0.492) for {'C': 564936.9748123022, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.798 (+/-0.492) for {'C': 564936.9748123022, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.798 (+/-0.492) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.797 (+/-0.492) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.798 (+/-0.492) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.798 (+/-0.492) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.798 (+/-0.492) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.848 (+/-0.460) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.798 (+/-0.492) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.798 (+/-0.492) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.798 (+/-0.492) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.798 (+/-0.492) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.798 (+/-0.492) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 1.0, 'kernel': 'linear'}
0.673 (+/-0.377) for {'C': 10.0, 'kernel': 'linear'}
0.631 (+/-0.312) for {'C': 100.0, 'kernel': 'linear'}
0.630 (+/-0.310) for {'C': 1000.0, 'kernel': 'linear'}
0.619 (+/-0.321) for {'C': 10000.0, 'kernel': 'linear'}
0.627 (+/-0.313) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.667 (+/-0.316) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.642 (+/-0.236) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.667 (+/-0.316) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.667 (+/-0.316) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.667 (+/-0.316) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.667 (+/-0.316) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.683 (+/-0.296) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.667 (+/-0.316) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.667 (+/-0.316) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.667 (+/-0.316) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.667 (+/-0.316) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.667 (+/-0.316) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.642 (+/-0.236) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.667 (+/-0.316) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.667 (+/-0.316) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.667 (+/-0.316) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.667 (+/-0.316) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.667 (+/-0.316) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.667 (+/-0.316) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.667 (+/-0.316) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.667 (+/-0.316) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.667 (+/-0.316) for {'C': 487528.4901033863, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.667 (+/-0.316) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.642 (+/-0.236) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.667 (+/-0.316) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.667 (+/-0.316) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.667 (+/-0.316) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.667 (+/-0.316) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.667 (+/-0.316) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.667 (+/-0.316) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.667 (+/-0.316) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.667 (+/-0.316) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.667 (+/-0.316) for {'C': 496592.32145033585, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.667 (+/-0.316) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.642 (+/-0.236) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.667 (+/-0.316) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.667 (+/-0.316) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.667 (+/-0.316) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.667 (+/-0.316) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.667 (+/-0.316) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.667 (+/-0.316) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.667 (+/-0.316) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.667 (+/-0.316) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.667 (+/-0.316) for {'C': 505824.6620031136, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.667 (+/-0.316) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.642 (+/-0.236) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.667 (+/-0.316) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.667 (+/-0.316) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.667 (+/-0.316) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.667 (+/-0.316) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.667 (+/-0.316) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.667 (+/-0.316) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.667 (+/-0.316) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.667 (+/-0.316) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.667 (+/-0.316) for {'C': 515228.64458175586, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.667 (+/-0.316) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.642 (+/-0.236) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.667 (+/-0.316) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.667 (+/-0.316) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.667 (+/-0.316) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.683 (+/-0.296) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.667 (+/-0.316) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.667 (+/-0.316) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.667 (+/-0.316) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.667 (+/-0.316) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.667 (+/-0.316) for {'C': 524807.4602497717, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.667 (+/-0.316) for {'C': 534564.3593969719, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.642 (+/-0.236) for {'C': 534564.3593969719, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.667 (+/-0.316) for {'C': 534564.3593969719, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.667 (+/-0.316) for {'C': 534564.3593969719, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.667 (+/-0.316) for {'C': 534564.3593969719, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.683 (+/-0.296) for {'C': 534564.3593969719, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.667 (+/-0.316) for {'C': 534564.3593969719, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.667 (+/-0.316) for {'C': 534564.3593969719, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.667 (+/-0.316) for {'C': 534564.3593969719, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.667 (+/-0.316) for {'C': 534564.3593969719, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.667 (+/-0.316) for {'C': 534564.3593969719, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.667 (+/-0.316) for {'C': 544502.6528424212, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.642 (+/-0.236) for {'C': 544502.6528424212, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.667 (+/-0.316) for {'C': 544502.6528424212, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.667 (+/-0.316) for {'C': 544502.6528424212, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.667 (+/-0.316) for {'C': 544502.6528424212, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.683 (+/-0.296) for {'C': 544502.6528424212, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.667 (+/-0.316) for {'C': 544502.6528424212, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.667 (+/-0.316) for {'C': 544502.6528424212, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.667 (+/-0.316) for {'C': 544502.6528424212, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.667 (+/-0.316) for {'C': 544502.6528424212, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.667 (+/-0.316) for {'C': 544502.6528424212, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.667 (+/-0.316) for {'C': 554625.7129579106, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.642 (+/-0.236) for {'C': 554625.7129579106, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.667 (+/-0.316) for {'C': 554625.7129579106, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.667 (+/-0.316) for {'C': 554625.7129579106, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.667 (+/-0.316) for {'C': 554625.7129579106, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.683 (+/-0.296) for {'C': 554625.7129579106, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.667 (+/-0.316) for {'C': 554625.7129579106, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.667 (+/-0.316) for {'C': 554625.7129579106, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.667 (+/-0.316) for {'C': 554625.7129579106, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.667 (+/-0.316) for {'C': 554625.7129579106, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.667 (+/-0.316) for {'C': 554625.7129579106, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.667 (+/-0.316) for {'C': 564936.9748123022, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.642 (+/-0.236) for {'C': 564936.9748123022, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.667 (+/-0.316) for {'C': 564936.9748123022, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.667 (+/-0.316) for {'C': 564936.9748123022, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.667 (+/-0.316) for {'C': 564936.9748123022, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.683 (+/-0.296) for {'C': 564936.9748123022, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.667 (+/-0.316) for {'C': 564936.9748123022, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.667 (+/-0.316) for {'C': 564936.9748123022, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.667 (+/-0.316) for {'C': 564936.9748123022, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.667 (+/-0.316) for {'C': 564936.9748123022, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.667 (+/-0.316) for {'C': 564936.9748123022, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.667 (+/-0.316) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.642 (+/-0.236) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.3334580622810035e-07}
0.667 (+/-0.316) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.3768402866248765e-07}
0.667 (+/-0.316) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.421029046736177e-07}
0.667 (+/-0.316) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.466039337234337e-07}
0.683 (+/-0.296) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.667 (+/-0.316) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.667 (+/-0.316) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.667 (+/-0.316) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.6546055619755397e-07}
0.667 (+/-0.316) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.703958364108843e-07}
0.667 (+/-0.316) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.663 (+/-0.316) for {'C': 10.0, 'kernel': 'linear'}
0.661 (+/-0.315) for {'C': 100.0, 'kernel': 'linear'}
0.637 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
0.611 (+/-0.241) for {'C': 10000.0, 'kernel': 'linear'}
0.636 (+/-0.237) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6833333333333332
发现最优参数C为原先的最大/最小值,直接重新设置超参。
第1轮loop中,tunemodel函数得到的最优参数是: ([{'C': array([4.78630092e+00, 4.78630092e+01, 4.78630092e+02, 4.78630092e+03,
4.78630092e+04, 4.78630092e+05, 4.78630092e+06, 4.78630092e+07,
4.78630092e+08, 4.78630092e+09, 4.78630092e+10]), 'kernel': ['rbf'], 'gamma': array([2.51188643e-07, 2.52115763e-07, 2.53046305e-07, 2.53980281e-07,
2.54917705e-07, 2.55858589e-07, 2.56802945e-07, 2.57750787e-07,
2.58702127e-07, 2.59656979e-07, 2.60615355e-07])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}], {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}, 0.6833333333333332)
这是第1次迭代微调C和gamma。
第1次迭代,得到delta: [4.61773679e+04 4.66994554e-09]
执行tunemodel()函数前,使用的grid search parameter是:
[{'C': array([4.78630092e+00, 4.78630092e+01, 4.78630092e+02, 4.78630092e+03,
4.78630092e+04, 4.78630092e+05, 4.78630092e+06, 4.78630092e+07,
4.78630092e+08, 4.78630092e+09, 4.78630092e+10]), 'kernel': ['rbf'], 'gamma': array([2.51188643e-07, 2.52115763e-07, 2.53046305e-07, 2.53980281e-07,
2.54917705e-07, 2.55858589e-07, 2.56802945e-07, 2.57750787e-07,
2.58702127e-07, 2.59656979e-07, 2.60615355e-07])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.496 (+/-0.001) for {'C': 4.7863009232263805, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.496 (+/-0.001) for {'C': 4.7863009232263805, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.496 (+/-0.001) for {'C': 4.7863009232263805, 'kernel': 'rbf', 'gamma': 2.530463049461392e-07}
0.496 (+/-0.001) for {'C': 4.7863009232263805, 'kernel': 'rbf', 'gamma': 2.539802813772814e-07}
0.496 (+/-0.001) for {'C': 4.7863009232263805, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.496 (+/-0.001) for {'C': 4.7863009232263805, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.496 (+/-0.001) for {'C': 4.7863009232263805, 'kernel': 'rbf', 'gamma': 2.568029450667348e-07}
0.496 (+/-0.001) for {'C': 4.7863009232263805, 'kernel': 'rbf', 'gamma': 2.577507869970538e-07}
0.496 (+/-0.001) for {'C': 4.7863009232263805, 'kernel': 'rbf', 'gamma': 2.587021273464608e-07}
0.496 (+/-0.001) for {'C': 4.7863009232263805, 'kernel': 'rbf', 'gamma': 2.596569790273783e-07}
0.496 (+/-0.001) for {'C': 4.7863009232263805, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.496 (+/-0.001) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.496 (+/-0.001) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.496 (+/-0.001) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 2.530463049461392e-07}
0.496 (+/-0.001) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 2.539802813772814e-07}
0.496 (+/-0.001) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.496 (+/-0.001) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.496 (+/-0.001) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 2.568029450667348e-07}
0.496 (+/-0.001) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 2.577507869970538e-07}
0.496 (+/-0.001) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 2.587021273464608e-07}
0.496 (+/-0.001) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 2.596569790273783e-07}
0.496 (+/-0.001) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.496 (+/-0.001) for {'C': 478.630092322638, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.496 (+/-0.001) for {'C': 478.630092322638, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.496 (+/-0.001) for {'C': 478.630092322638, 'kernel': 'rbf', 'gamma': 2.530463049461392e-07}
0.496 (+/-0.001) for {'C': 478.630092322638, 'kernel': 'rbf', 'gamma': 2.539802813772814e-07}
0.496 (+/-0.001) for {'C': 478.630092322638, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.496 (+/-0.001) for {'C': 478.630092322638, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.496 (+/-0.001) for {'C': 478.630092322638, 'kernel': 'rbf', 'gamma': 2.568029450667348e-07}
0.496 (+/-0.001) for {'C': 478.630092322638, 'kernel': 'rbf', 'gamma': 2.577507869970538e-07}
0.496 (+/-0.001) for {'C': 478.630092322638, 'kernel': 'rbf', 'gamma': 2.587021273464608e-07}
0.496 (+/-0.001) for {'C': 478.630092322638, 'kernel': 'rbf', 'gamma': 2.596569790273783e-07}
0.496 (+/-0.001) for {'C': 478.630092322638, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.496 (+/-0.001) for {'C': 4786.30092322638, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.496 (+/-0.001) for {'C': 4786.30092322638, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.496 (+/-0.001) for {'C': 4786.30092322638, 'kernel': 'rbf', 'gamma': 2.530463049461392e-07}
0.496 (+/-0.001) for {'C': 4786.30092322638, 'kernel': 'rbf', 'gamma': 2.539802813772814e-07}
0.496 (+/-0.001) for {'C': 4786.30092322638, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.496 (+/-0.001) for {'C': 4786.30092322638, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.496 (+/-0.001) for {'C': 4786.30092322638, 'kernel': 'rbf', 'gamma': 2.568029450667348e-07}
0.496 (+/-0.001) for {'C': 4786.30092322638, 'kernel': 'rbf', 'gamma': 2.577507869970538e-07}
0.496 (+/-0.001) for {'C': 4786.30092322638, 'kernel': 'rbf', 'gamma': 2.587021273464608e-07}
0.496 (+/-0.001) for {'C': 4786.30092322638, 'kernel': 'rbf', 'gamma': 2.596569790273783e-07}
0.496 (+/-0.001) for {'C': 4786.30092322638, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.496 (+/-0.001) for {'C': 47863.0092322638, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.496 (+/-0.001) for {'C': 47863.0092322638, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.496 (+/-0.001) for {'C': 47863.0092322638, 'kernel': 'rbf', 'gamma': 2.530463049461392e-07}
0.496 (+/-0.001) for {'C': 47863.0092322638, 'kernel': 'rbf', 'gamma': 2.539802813772814e-07}
0.547 (+/-0.301) for {'C': 47863.0092322638, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.547 (+/-0.301) for {'C': 47863.0092322638, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.547 (+/-0.301) for {'C': 47863.0092322638, 'kernel': 'rbf', 'gamma': 2.568029450667348e-07}
0.547 (+/-0.301) for {'C': 47863.0092322638, 'kernel': 'rbf', 'gamma': 2.577507869970538e-07}
0.547 (+/-0.301) for {'C': 47863.0092322638, 'kernel': 'rbf', 'gamma': 2.587021273464608e-07}
0.547 (+/-0.301) for {'C': 47863.0092322638, 'kernel': 'rbf', 'gamma': 2.596569790273783e-07}
0.547 (+/-0.301) for {'C': 47863.0092322638, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.797 (+/-0.492) for {'C': 478630.092322638, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.797 (+/-0.492) for {'C': 478630.092322638, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.797 (+/-0.492) for {'C': 478630.092322638, 'kernel': 'rbf', 'gamma': 2.530463049461392e-07}
0.797 (+/-0.492) for {'C': 478630.092322638, 'kernel': 'rbf', 'gamma': 2.539802813772814e-07}
0.797 (+/-0.492) for {'C': 478630.092322638, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.797 (+/-0.492) for {'C': 478630.092322638, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.797 (+/-0.492) for {'C': 478630.092322638, 'kernel': 'rbf', 'gamma': 2.568029450667348e-07}
0.797 (+/-0.492) for {'C': 478630.092322638, 'kernel': 'rbf', 'gamma': 2.577507869970538e-07}
0.797 (+/-0.492) for {'C': 478630.092322638, 'kernel': 'rbf', 'gamma': 2.587021273464608e-07}
0.797 (+/-0.492) for {'C': 478630.092322638, 'kernel': 'rbf', 'gamma': 2.596569790273783e-07}
0.797 (+/-0.492) for {'C': 478630.092322638, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.810 (+/-0.465) for {'C': 4786300.923226381, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.773 (+/-0.474) for {'C': 4786300.923226381, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.760 (+/-0.482) for {'C': 4786300.923226381, 'kernel': 'rbf', 'gamma': 2.530463049461392e-07}
0.693 (+/-0.446) for {'C': 4786300.923226381, 'kernel': 'rbf', 'gamma': 2.539802813772814e-07}
0.760 (+/-0.482) for {'C': 4786300.923226381, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.743 (+/-0.459) for {'C': 4786300.923226381, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.743 (+/-0.459) for {'C': 4786300.923226381, 'kernel': 'rbf', 'gamma': 2.568029450667348e-07}
0.760 (+/-0.482) for {'C': 4786300.923226381, 'kernel': 'rbf', 'gamma': 2.577507869970538e-07}
0.773 (+/-0.474) for {'C': 4786300.923226381, 'kernel': 'rbf', 'gamma': 2.587021273464608e-07}
0.773 (+/-0.474) for {'C': 4786300.923226381, 'kernel': 'rbf', 'gamma': 2.596569790273783e-07}
0.743 (+/-0.459) for {'C': 4786300.923226381, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.736 (+/-0.468) for {'C': 47863009.2322638, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.686 (+/-0.452) for {'C': 47863009.2322638, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.736 (+/-0.468) for {'C': 47863009.2322638, 'kernel': 'rbf', 'gamma': 2.530463049461392e-07}
0.753 (+/-0.492) for {'C': 47863009.2322638, 'kernel': 'rbf', 'gamma': 2.539802813772814e-07}
0.688 (+/-0.390) for {'C': 47863009.2322638, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.741 (+/-0.402) for {'C': 47863009.2322638, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.741 (+/-0.402) for {'C': 47863009.2322638, 'kernel': 'rbf', 'gamma': 2.568029450667348e-07}
0.691 (+/-0.387) for {'C': 47863009.2322638, 'kernel': 'rbf', 'gamma': 2.577507869970538e-07}
0.679 (+/-0.389) for {'C': 47863009.2322638, 'kernel': 'rbf', 'gamma': 2.587021273464608e-07}
0.674 (+/-0.392) for {'C': 47863009.2322638, 'kernel': 'rbf', 'gamma': 2.596569790273783e-07}
0.678 (+/-0.391) for {'C': 47863009.2322638, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.736 (+/-0.468) for {'C': 478630092.32263803, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.686 (+/-0.452) for {'C': 478630092.32263803, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.736 (+/-0.468) for {'C': 478630092.32263803, 'kernel': 'rbf', 'gamma': 2.530463049461392e-07}
0.736 (+/-0.468) for {'C': 478630092.32263803, 'kernel': 'rbf', 'gamma': 2.539802813772814e-07}
0.686 (+/-0.393) for {'C': 478630092.32263803, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.736 (+/-0.411) for {'C': 478630092.32263803, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.731 (+/-0.407) for {'C': 478630092.32263803, 'kernel': 'rbf', 'gamma': 2.568029450667348e-07}
0.733 (+/-0.405) for {'C': 478630092.32263803, 'kernel': 'rbf', 'gamma': 2.577507869970538e-07}
0.712 (+/-0.372) for {'C': 478630092.32263803, 'kernel': 'rbf', 'gamma': 2.587021273464608e-07}
0.686 (+/-0.393) for {'C': 478630092.32263803, 'kernel': 'rbf', 'gamma': 2.596569790273783e-07}
0.686 (+/-0.393) for {'C': 478630092.32263803, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.736 (+/-0.468) for {'C': 4786300923.22638, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.686 (+/-0.452) for {'C': 4786300923.22638, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.736 (+/-0.468) for {'C': 4786300923.22638, 'kernel': 'rbf', 'gamma': 2.530463049461392e-07}
0.753 (+/-0.492) for {'C': 4786300923.22638, 'kernel': 'rbf', 'gamma': 2.539802813772814e-07}
0.686 (+/-0.393) for {'C': 4786300923.22638, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.739 (+/-0.405) for {'C': 4786300923.22638, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.731 (+/-0.407) for {'C': 4786300923.22638, 'kernel': 'rbf', 'gamma': 2.568029450667348e-07}
0.731 (+/-0.407) for {'C': 4786300923.22638, 'kernel': 'rbf', 'gamma': 2.577507869970538e-07}
0.712 (+/-0.372) for {'C': 4786300923.22638, 'kernel': 'rbf', 'gamma': 2.587021273464608e-07}
0.686 (+/-0.393) for {'C': 4786300923.22638, 'kernel': 'rbf', 'gamma': 2.596569790273783e-07}
0.686 (+/-0.393) for {'C': 4786300923.22638, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.736 (+/-0.468) for {'C': 47863009232.2638, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.686 (+/-0.452) for {'C': 47863009232.2638, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.736 (+/-0.468) for {'C': 47863009232.2638, 'kernel': 'rbf', 'gamma': 2.530463049461392e-07}
0.753 (+/-0.492) for {'C': 47863009232.2638, 'kernel': 'rbf', 'gamma': 2.539802813772814e-07}
0.686 (+/-0.393) for {'C': 47863009232.2638, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.736 (+/-0.411) for {'C': 47863009232.2638, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.731 (+/-0.407) for {'C': 47863009232.2638, 'kernel': 'rbf', 'gamma': 2.568029450667348e-07}
0.731 (+/-0.407) for {'C': 47863009232.2638, 'kernel': 'rbf', 'gamma': 2.577507869970538e-07}
0.712 (+/-0.372) for {'C': 47863009232.2638, 'kernel': 'rbf', 'gamma': 2.587021273464608e-07}
0.686 (+/-0.393) for {'C': 47863009232.2638, 'kernel': 'rbf', 'gamma': 2.596569790273783e-07}
0.686 (+/-0.393) for {'C': 47863009232.2638, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'linear'}
0.706 (+/-0.383) for {'C': 10.0, 'kernel': 'linear'}
0.643 (+/-0.320) for {'C': 100.0, 'kernel': 'linear'}
0.630 (+/-0.310) for {'C': 1000.0, 'kernel': 'linear'}
0.619 (+/-0.321) for {'C': 10000.0, 'kernel': 'linear'}
0.627 (+/-0.313) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.500 (+/-0.000) for {'C': 4.7863009232263805, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.500 (+/-0.000) for {'C': 4.7863009232263805, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.500 (+/-0.000) for {'C': 4.7863009232263805, 'kernel': 'rbf', 'gamma': 2.530463049461392e-07}
0.500 (+/-0.000) for {'C': 4.7863009232263805, 'kernel': 'rbf', 'gamma': 2.539802813772814e-07}
0.500 (+/-0.000) for {'C': 4.7863009232263805, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.500 (+/-0.000) for {'C': 4.7863009232263805, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.500 (+/-0.000) for {'C': 4.7863009232263805, 'kernel': 'rbf', 'gamma': 2.568029450667348e-07}
0.500 (+/-0.000) for {'C': 4.7863009232263805, 'kernel': 'rbf', 'gamma': 2.577507869970538e-07}
0.500 (+/-0.000) for {'C': 4.7863009232263805, 'kernel': 'rbf', 'gamma': 2.587021273464608e-07}
0.500 (+/-0.000) for {'C': 4.7863009232263805, 'kernel': 'rbf', 'gamma': 2.596569790273783e-07}
0.500 (+/-0.000) for {'C': 4.7863009232263805, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.500 (+/-0.000) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.500 (+/-0.000) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.500 (+/-0.000) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 2.530463049461392e-07}
0.500 (+/-0.000) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 2.539802813772814e-07}
0.500 (+/-0.000) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.500 (+/-0.000) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.500 (+/-0.000) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 2.568029450667348e-07}
0.500 (+/-0.000) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 2.577507869970538e-07}
0.500 (+/-0.000) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 2.587021273464608e-07}
0.500 (+/-0.000) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 2.596569790273783e-07}
0.500 (+/-0.000) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.500 (+/-0.000) for {'C': 478.630092322638, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.500 (+/-0.000) for {'C': 478.630092322638, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.500 (+/-0.000) for {'C': 478.630092322638, 'kernel': 'rbf', 'gamma': 2.530463049461392e-07}
0.500 (+/-0.000) for {'C': 478.630092322638, 'kernel': 'rbf', 'gamma': 2.539802813772814e-07}
0.500 (+/-0.000) for {'C': 478.630092322638, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.500 (+/-0.000) for {'C': 478.630092322638, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.500 (+/-0.000) for {'C': 478.630092322638, 'kernel': 'rbf', 'gamma': 2.568029450667348e-07}
0.500 (+/-0.000) for {'C': 478.630092322638, 'kernel': 'rbf', 'gamma': 2.577507869970538e-07}
0.500 (+/-0.000) for {'C': 478.630092322638, 'kernel': 'rbf', 'gamma': 2.587021273464608e-07}
0.500 (+/-0.000) for {'C': 478.630092322638, 'kernel': 'rbf', 'gamma': 2.596569790273783e-07}
0.500 (+/-0.000) for {'C': 478.630092322638, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.500 (+/-0.000) for {'C': 4786.30092322638, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.500 (+/-0.000) for {'C': 4786.30092322638, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.500 (+/-0.000) for {'C': 4786.30092322638, 'kernel': 'rbf', 'gamma': 2.530463049461392e-07}
0.500 (+/-0.000) for {'C': 4786.30092322638, 'kernel': 'rbf', 'gamma': 2.539802813772814e-07}
0.500 (+/-0.000) for {'C': 4786.30092322638, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.500 (+/-0.000) for {'C': 4786.30092322638, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.500 (+/-0.000) for {'C': 4786.30092322638, 'kernel': 'rbf', 'gamma': 2.568029450667348e-07}
0.500 (+/-0.000) for {'C': 4786.30092322638, 'kernel': 'rbf', 'gamma': 2.577507869970538e-07}
0.500 (+/-0.000) for {'C': 4786.30092322638, 'kernel': 'rbf', 'gamma': 2.587021273464608e-07}
0.500 (+/-0.000) for {'C': 4786.30092322638, 'kernel': 'rbf', 'gamma': 2.596569790273783e-07}
0.500 (+/-0.000) for {'C': 4786.30092322638, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.500 (+/-0.000) for {'C': 47863.0092322638, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.500 (+/-0.000) for {'C': 47863.0092322638, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.500 (+/-0.000) for {'C': 47863.0092322638, 'kernel': 'rbf', 'gamma': 2.530463049461392e-07}
0.500 (+/-0.000) for {'C': 47863.0092322638, 'kernel': 'rbf', 'gamma': 2.539802813772814e-07}
0.525 (+/-0.150) for {'C': 47863.0092322638, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.525 (+/-0.150) for {'C': 47863.0092322638, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.525 (+/-0.150) for {'C': 47863.0092322638, 'kernel': 'rbf', 'gamma': 2.568029450667348e-07}
0.525 (+/-0.150) for {'C': 47863.0092322638, 'kernel': 'rbf', 'gamma': 2.577507869970538e-07}
0.525 (+/-0.150) for {'C': 47863.0092322638, 'kernel': 'rbf', 'gamma': 2.587021273464608e-07}
0.525 (+/-0.150) for {'C': 47863.0092322638, 'kernel': 'rbf', 'gamma': 2.596569790273783e-07}
0.525 (+/-0.150) for {'C': 47863.0092322638, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.642 (+/-0.236) for {'C': 478630.092322638, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.642 (+/-0.236) for {'C': 478630.092322638, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.642 (+/-0.236) for {'C': 478630.092322638, 'kernel': 'rbf', 'gamma': 2.530463049461392e-07}
0.642 (+/-0.236) for {'C': 478630.092322638, 'kernel': 'rbf', 'gamma': 2.539802813772814e-07}
0.642 (+/-0.236) for {'C': 478630.092322638, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.642 (+/-0.236) for {'C': 478630.092322638, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.642 (+/-0.236) for {'C': 478630.092322638, 'kernel': 'rbf', 'gamma': 2.568029450667348e-07}
0.642 (+/-0.236) for {'C': 478630.092322638, 'kernel': 'rbf', 'gamma': 2.577507869970538e-07}
0.642 (+/-0.236) for {'C': 478630.092322638, 'kernel': 'rbf', 'gamma': 2.587021273464608e-07}
0.642 (+/-0.236) for {'C': 478630.092322638, 'kernel': 'rbf', 'gamma': 2.596569790273783e-07}
0.642 (+/-0.236) for {'C': 478630.092322638, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.683 (+/-0.296) for {'C': 4786300.923226381, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.667 (+/-0.316) for {'C': 4786300.923226381, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.666 (+/-0.316) for {'C': 4786300.923226381, 'kernel': 'rbf', 'gamma': 2.530463049461392e-07}
0.641 (+/-0.324) for {'C': 4786300.923226381, 'kernel': 'rbf', 'gamma': 2.539802813772814e-07}
0.666 (+/-0.316) for {'C': 4786300.923226381, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.666 (+/-0.315) for {'C': 4786300.923226381, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.666 (+/-0.315) for {'C': 4786300.923226381, 'kernel': 'rbf', 'gamma': 2.568029450667348e-07}
0.666 (+/-0.316) for {'C': 4786300.923226381, 'kernel': 'rbf', 'gamma': 2.577507869970538e-07}
0.667 (+/-0.316) for {'C': 4786300.923226381, 'kernel': 'rbf', 'gamma': 2.587021273464608e-07}
0.667 (+/-0.316) for {'C': 4786300.923226381, 'kernel': 'rbf', 'gamma': 2.596569790273783e-07}
0.666 (+/-0.315) for {'C': 4786300.923226381, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.657 (+/-0.309) for {'C': 47863009.2322638, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.632 (+/-0.315) for {'C': 47863009.2322638, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.657 (+/-0.309) for {'C': 47863009.2322638, 'kernel': 'rbf', 'gamma': 2.530463049461392e-07}
0.657 (+/-0.310) for {'C': 47863009.2322638, 'kernel': 'rbf', 'gamma': 2.539802813772814e-07}
0.665 (+/-0.314) for {'C': 47863009.2322638, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.682 (+/-0.295) for {'C': 47863009.2322638, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.682 (+/-0.295) for {'C': 47863009.2322638, 'kernel': 'rbf', 'gamma': 2.568029450667348e-07}
0.666 (+/-0.315) for {'C': 47863009.2322638, 'kernel': 'rbf', 'gamma': 2.577507869970538e-07}
0.665 (+/-0.314) for {'C': 47863009.2322638, 'kernel': 'rbf', 'gamma': 2.587021273464608e-07}
0.665 (+/-0.314) for {'C': 47863009.2322638, 'kernel': 'rbf', 'gamma': 2.596569790273783e-07}
0.665 (+/-0.314) for {'C': 47863009.2322638, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.657 (+/-0.309) for {'C': 478630092.32263803, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.632 (+/-0.315) for {'C': 478630092.32263803, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.657 (+/-0.309) for {'C': 478630092.32263803, 'kernel': 'rbf', 'gamma': 2.530463049461392e-07}
0.657 (+/-0.309) for {'C': 478630092.32263803, 'kernel': 'rbf', 'gamma': 2.539802813772814e-07}
0.665 (+/-0.314) for {'C': 478630092.32263803, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.681 (+/-0.294) for {'C': 478630092.32263803, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.682 (+/-0.294) for {'C': 478630092.32263803, 'kernel': 'rbf', 'gamma': 2.568029450667348e-07}
0.682 (+/-0.295) for {'C': 478630092.32263803, 'kernel': 'rbf', 'gamma': 2.577507869970538e-07}
0.681 (+/-0.294) for {'C': 478630092.32263803, 'kernel': 'rbf', 'gamma': 2.587021273464608e-07}
0.665 (+/-0.314) for {'C': 478630092.32263803, 'kernel': 'rbf', 'gamma': 2.596569790273783e-07}
0.665 (+/-0.314) for {'C': 478630092.32263803, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.657 (+/-0.309) for {'C': 4786300923.22638, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.632 (+/-0.315) for {'C': 4786300923.22638, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.657 (+/-0.309) for {'C': 4786300923.22638, 'kernel': 'rbf', 'gamma': 2.530463049461392e-07}
0.657 (+/-0.310) for {'C': 4786300923.22638, 'kernel': 'rbf', 'gamma': 2.539802813772814e-07}
0.665 (+/-0.314) for {'C': 4786300923.22638, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.682 (+/-0.295) for {'C': 4786300923.22638, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.682 (+/-0.294) for {'C': 4786300923.22638, 'kernel': 'rbf', 'gamma': 2.568029450667348e-07}
0.682 (+/-0.294) for {'C': 4786300923.22638, 'kernel': 'rbf', 'gamma': 2.577507869970538e-07}
0.681 (+/-0.294) for {'C': 4786300923.22638, 'kernel': 'rbf', 'gamma': 2.587021273464608e-07}
0.665 (+/-0.314) for {'C': 4786300923.22638, 'kernel': 'rbf', 'gamma': 2.596569790273783e-07}
0.665 (+/-0.314) for {'C': 4786300923.22638, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.657 (+/-0.309) for {'C': 47863009232.2638, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.632 (+/-0.315) for {'C': 47863009232.2638, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.657 (+/-0.309) for {'C': 47863009232.2638, 'kernel': 'rbf', 'gamma': 2.530463049461392e-07}
0.657 (+/-0.310) for {'C': 47863009232.2638, 'kernel': 'rbf', 'gamma': 2.539802813772814e-07}
0.665 (+/-0.314) for {'C': 47863009232.2638, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.681 (+/-0.294) for {'C': 47863009232.2638, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.682 (+/-0.294) for {'C': 47863009232.2638, 'kernel': 'rbf', 'gamma': 2.568029450667348e-07}
0.682 (+/-0.294) for {'C': 47863009232.2638, 'kernel': 'rbf', 'gamma': 2.577507869970538e-07}
0.681 (+/-0.294) for {'C': 47863009232.2638, 'kernel': 'rbf', 'gamma': 2.587021273464608e-07}
0.665 (+/-0.314) for {'C': 47863009232.2638, 'kernel': 'rbf', 'gamma': 2.596569790273783e-07}
0.665 (+/-0.314) for {'C': 47863009232.2638, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'linear'}
0.666 (+/-0.315) for {'C': 10.0, 'kernel': 'linear'}
0.662 (+/-0.317) for {'C': 100.0, 'kernel': 'linear'}
0.637 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
0.611 (+/-0.241) for {'C': 10000.0, 'kernel': 'linear'}
0.636 (+/-0.237) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 4786300.923226381, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6828525641025641
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.496 (+/-0.001) for {'C': 4.7863009232263805, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.496 (+/-0.001) for {'C': 4.7863009232263805, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.496 (+/-0.001) for {'C': 4.7863009232263805, 'kernel': 'rbf', 'gamma': 2.530463049461392e-07}
0.496 (+/-0.001) for {'C': 4.7863009232263805, 'kernel': 'rbf', 'gamma': 2.539802813772814e-07}
0.496 (+/-0.001) for {'C': 4.7863009232263805, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.496 (+/-0.001) for {'C': 4.7863009232263805, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.496 (+/-0.001) for {'C': 4.7863009232263805, 'kernel': 'rbf', 'gamma': 2.568029450667348e-07}
0.496 (+/-0.001) for {'C': 4.7863009232263805, 'kernel': 'rbf', 'gamma': 2.577507869970538e-07}
0.496 (+/-0.001) for {'C': 4.7863009232263805, 'kernel': 'rbf', 'gamma': 2.587021273464608e-07}
0.496 (+/-0.001) for {'C': 4.7863009232263805, 'kernel': 'rbf', 'gamma': 2.596569790273783e-07}
0.496 (+/-0.001) for {'C': 4.7863009232263805, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.496 (+/-0.001) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.496 (+/-0.001) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.496 (+/-0.001) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 2.530463049461392e-07}
0.496 (+/-0.001) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 2.539802813772814e-07}
0.496 (+/-0.001) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.496 (+/-0.001) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.496 (+/-0.001) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 2.568029450667348e-07}
0.496 (+/-0.001) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 2.577507869970538e-07}
0.496 (+/-0.001) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 2.587021273464608e-07}
0.496 (+/-0.001) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 2.596569790273783e-07}
0.496 (+/-0.001) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.496 (+/-0.001) for {'C': 478.630092322638, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.496 (+/-0.001) for {'C': 478.630092322638, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.496 (+/-0.001) for {'C': 478.630092322638, 'kernel': 'rbf', 'gamma': 2.530463049461392e-07}
0.496 (+/-0.001) for {'C': 478.630092322638, 'kernel': 'rbf', 'gamma': 2.539802813772814e-07}
0.496 (+/-0.001) for {'C': 478.630092322638, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.496 (+/-0.001) for {'C': 478.630092322638, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.496 (+/-0.001) for {'C': 478.630092322638, 'kernel': 'rbf', 'gamma': 2.568029450667348e-07}
0.496 (+/-0.001) for {'C': 478.630092322638, 'kernel': 'rbf', 'gamma': 2.577507869970538e-07}
0.496 (+/-0.001) for {'C': 478.630092322638, 'kernel': 'rbf', 'gamma': 2.587021273464608e-07}
0.496 (+/-0.001) for {'C': 478.630092322638, 'kernel': 'rbf', 'gamma': 2.596569790273783e-07}
0.496 (+/-0.001) for {'C': 478.630092322638, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.496 (+/-0.001) for {'C': 4786.30092322638, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.496 (+/-0.001) for {'C': 4786.30092322638, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.496 (+/-0.001) for {'C': 4786.30092322638, 'kernel': 'rbf', 'gamma': 2.530463049461392e-07}
0.496 (+/-0.001) for {'C': 4786.30092322638, 'kernel': 'rbf', 'gamma': 2.539802813772814e-07}
0.496 (+/-0.001) for {'C': 4786.30092322638, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.496 (+/-0.001) for {'C': 4786.30092322638, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.496 (+/-0.001) for {'C': 4786.30092322638, 'kernel': 'rbf', 'gamma': 2.568029450667348e-07}
0.496 (+/-0.001) for {'C': 4786.30092322638, 'kernel': 'rbf', 'gamma': 2.577507869970538e-07}
0.496 (+/-0.001) for {'C': 4786.30092322638, 'kernel': 'rbf', 'gamma': 2.587021273464608e-07}
0.496 (+/-0.001) for {'C': 4786.30092322638, 'kernel': 'rbf', 'gamma': 2.596569790273783e-07}
0.496 (+/-0.001) for {'C': 4786.30092322638, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.747 (+/-0.502) for {'C': 47863.0092322638, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.747 (+/-0.502) for {'C': 47863.0092322638, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.747 (+/-0.502) for {'C': 47863.0092322638, 'kernel': 'rbf', 'gamma': 2.530463049461392e-07}
0.747 (+/-0.502) for {'C': 47863.0092322638, 'kernel': 'rbf', 'gamma': 2.539802813772814e-07}
0.747 (+/-0.502) for {'C': 47863.0092322638, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.747 (+/-0.502) for {'C': 47863.0092322638, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.747 (+/-0.502) for {'C': 47863.0092322638, 'kernel': 'rbf', 'gamma': 2.568029450667348e-07}
0.747 (+/-0.502) for {'C': 47863.0092322638, 'kernel': 'rbf', 'gamma': 2.577507869970538e-07}
0.747 (+/-0.502) for {'C': 47863.0092322638, 'kernel': 'rbf', 'gamma': 2.587021273464608e-07}
0.747 (+/-0.502) for {'C': 47863.0092322638, 'kernel': 'rbf', 'gamma': 2.596569790273783e-07}
0.747 (+/-0.502) for {'C': 47863.0092322638, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.798 (+/-0.492) for {'C': 478630.092322638, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.848 (+/-0.460) for {'C': 478630.092322638, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.798 (+/-0.492) for {'C': 478630.092322638, 'kernel': 'rbf', 'gamma': 2.530463049461392e-07}
0.798 (+/-0.492) for {'C': 478630.092322638, 'kernel': 'rbf', 'gamma': 2.539802813772814e-07}
0.798 (+/-0.492) for {'C': 478630.092322638, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.848 (+/-0.460) for {'C': 478630.092322638, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.798 (+/-0.492) for {'C': 478630.092322638, 'kernel': 'rbf', 'gamma': 2.568029450667348e-07}
0.798 (+/-0.492) for {'C': 478630.092322638, 'kernel': 'rbf', 'gamma': 2.577507869970538e-07}
0.798 (+/-0.492) for {'C': 478630.092322638, 'kernel': 'rbf', 'gamma': 2.587021273464608e-07}
0.798 (+/-0.492) for {'C': 478630.092322638, 'kernel': 'rbf', 'gamma': 2.596569790273783e-07}
0.798 (+/-0.492) for {'C': 478630.092322638, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.696 (+/-0.439) for {'C': 4786300.923226381, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.646 (+/-0.403) for {'C': 4786300.923226381, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.662 (+/-0.444) for {'C': 4786300.923226381, 'kernel': 'rbf', 'gamma': 2.530463049461392e-07}
0.662 (+/-0.444) for {'C': 4786300.923226381, 'kernel': 'rbf', 'gamma': 2.539802813772814e-07}
0.663 (+/-0.391) for {'C': 4786300.923226381, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.688 (+/-0.432) for {'C': 4786300.923226381, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.688 (+/-0.432) for {'C': 4786300.923226381, 'kernel': 'rbf', 'gamma': 2.568029450667348e-07}
0.669 (+/-0.395) for {'C': 4786300.923226381, 'kernel': 'rbf', 'gamma': 2.577507869970538e-07}
0.668 (+/-0.396) for {'C': 4786300.923226381, 'kernel': 'rbf', 'gamma': 2.587021273464608e-07}
0.668 (+/-0.396) for {'C': 4786300.923226381, 'kernel': 'rbf', 'gamma': 2.596569790273783e-07}
0.669 (+/-0.395) for {'C': 4786300.923226381, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.650 (+/-0.397) for {'C': 47863009.2322638, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.650 (+/-0.397) for {'C': 47863009.2322638, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.655 (+/-0.394) for {'C': 47863009.2322638, 'kernel': 'rbf', 'gamma': 2.530463049461392e-07}
0.642 (+/-0.405) for {'C': 47863009.2322638, 'kernel': 'rbf', 'gamma': 2.539802813772814e-07}
0.648 (+/-0.398) for {'C': 47863009.2322638, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.654 (+/-0.394) for {'C': 47863009.2322638, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.651 (+/-0.396) for {'C': 47863009.2322638, 'kernel': 'rbf', 'gamma': 2.568029450667348e-07}
0.658 (+/-0.395) for {'C': 47863009.2322638, 'kernel': 'rbf', 'gamma': 2.577507869970538e-07}
0.651 (+/-0.396) for {'C': 47863009.2322638, 'kernel': 'rbf', 'gamma': 2.587021273464608e-07}
0.651 (+/-0.396) for {'C': 47863009.2322638, 'kernel': 'rbf', 'gamma': 2.596569790273783e-07}
0.654 (+/-0.395) for {'C': 47863009.2322638, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.625 (+/-0.332) for {'C': 478630092.32263803, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.650 (+/-0.397) for {'C': 478630092.32263803, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.654 (+/-0.395) for {'C': 478630092.32263803, 'kernel': 'rbf', 'gamma': 2.530463049461392e-07}
0.642 (+/-0.405) for {'C': 478630092.32263803, 'kernel': 'rbf', 'gamma': 2.539802813772814e-07}
0.623 (+/-0.334) for {'C': 478630092.32263803, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.628 (+/-0.331) for {'C': 478630092.32263803, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.650 (+/-0.397) for {'C': 478630092.32263803, 'kernel': 'rbf', 'gamma': 2.568029450667348e-07}
0.654 (+/-0.395) for {'C': 478630092.32263803, 'kernel': 'rbf', 'gamma': 2.577507869970538e-07}
0.651 (+/-0.396) for {'C': 478630092.32263803, 'kernel': 'rbf', 'gamma': 2.587021273464608e-07}
0.650 (+/-0.397) for {'C': 478630092.32263803, 'kernel': 'rbf', 'gamma': 2.596569790273783e-07}
0.654 (+/-0.395) for {'C': 478630092.32263803, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.625 (+/-0.332) for {'C': 4786300923.22638, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.650 (+/-0.397) for {'C': 4786300923.22638, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.654 (+/-0.395) for {'C': 4786300923.22638, 'kernel': 'rbf', 'gamma': 2.530463049461392e-07}
0.642 (+/-0.406) for {'C': 4786300923.22638, 'kernel': 'rbf', 'gamma': 2.539802813772814e-07}
0.623 (+/-0.334) for {'C': 4786300923.22638, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.627 (+/-0.331) for {'C': 4786300923.22638, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.650 (+/-0.398) for {'C': 4786300923.22638, 'kernel': 'rbf', 'gamma': 2.568029450667348e-07}
0.653 (+/-0.396) for {'C': 4786300923.22638, 'kernel': 'rbf', 'gamma': 2.577507869970538e-07}
0.651 (+/-0.396) for {'C': 4786300923.22638, 'kernel': 'rbf', 'gamma': 2.587021273464608e-07}
0.650 (+/-0.397) for {'C': 4786300923.22638, 'kernel': 'rbf', 'gamma': 2.596569790273783e-07}
0.654 (+/-0.395) for {'C': 4786300923.22638, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.625 (+/-0.332) for {'C': 47863009232.2638, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.650 (+/-0.397) for {'C': 47863009232.2638, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.654 (+/-0.395) for {'C': 47863009232.2638, 'kernel': 'rbf', 'gamma': 2.530463049461392e-07}
0.641 (+/-0.406) for {'C': 47863009232.2638, 'kernel': 'rbf', 'gamma': 2.539802813772814e-07}
0.623 (+/-0.334) for {'C': 47863009232.2638, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.627 (+/-0.331) for {'C': 47863009232.2638, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.650 (+/-0.398) for {'C': 47863009232.2638, 'kernel': 'rbf', 'gamma': 2.568029450667348e-07}
0.653 (+/-0.396) for {'C': 47863009232.2638, 'kernel': 'rbf', 'gamma': 2.577507869970538e-07}
0.651 (+/-0.396) for {'C': 47863009232.2638, 'kernel': 'rbf', 'gamma': 2.587021273464608e-07}
0.650 (+/-0.397) for {'C': 47863009232.2638, 'kernel': 'rbf', 'gamma': 2.596569790273783e-07}
0.654 (+/-0.395) for {'C': 47863009232.2638, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 1.0, 'kernel': 'linear'}
0.673 (+/-0.377) for {'C': 10.0, 'kernel': 'linear'}
0.631 (+/-0.312) for {'C': 100.0, 'kernel': 'linear'}
0.630 (+/-0.310) for {'C': 1000.0, 'kernel': 'linear'}
0.619 (+/-0.321) for {'C': 10000.0, 'kernel': 'linear'}
0.627 (+/-0.313) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.500 (+/-0.000) for {'C': 4.7863009232263805, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.500 (+/-0.000) for {'C': 4.7863009232263805, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.500 (+/-0.000) for {'C': 4.7863009232263805, 'kernel': 'rbf', 'gamma': 2.530463049461392e-07}
0.500 (+/-0.000) for {'C': 4.7863009232263805, 'kernel': 'rbf', 'gamma': 2.539802813772814e-07}
0.500 (+/-0.000) for {'C': 4.7863009232263805, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.500 (+/-0.000) for {'C': 4.7863009232263805, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.500 (+/-0.000) for {'C': 4.7863009232263805, 'kernel': 'rbf', 'gamma': 2.568029450667348e-07}
0.500 (+/-0.000) for {'C': 4.7863009232263805, 'kernel': 'rbf', 'gamma': 2.577507869970538e-07}
0.500 (+/-0.000) for {'C': 4.7863009232263805, 'kernel': 'rbf', 'gamma': 2.587021273464608e-07}
0.500 (+/-0.000) for {'C': 4.7863009232263805, 'kernel': 'rbf', 'gamma': 2.596569790273783e-07}
0.500 (+/-0.000) for {'C': 4.7863009232263805, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.500 (+/-0.000) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.500 (+/-0.000) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.500 (+/-0.000) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 2.530463049461392e-07}
0.500 (+/-0.000) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 2.539802813772814e-07}
0.500 (+/-0.000) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.500 (+/-0.000) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.500 (+/-0.000) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 2.568029450667348e-07}
0.500 (+/-0.000) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 2.577507869970538e-07}
0.500 (+/-0.000) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 2.587021273464608e-07}
0.500 (+/-0.000) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 2.596569790273783e-07}
0.500 (+/-0.000) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.500 (+/-0.000) for {'C': 478.630092322638, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.500 (+/-0.000) for {'C': 478.630092322638, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.500 (+/-0.000) for {'C': 478.630092322638, 'kernel': 'rbf', 'gamma': 2.530463049461392e-07}
0.500 (+/-0.000) for {'C': 478.630092322638, 'kernel': 'rbf', 'gamma': 2.539802813772814e-07}
0.500 (+/-0.000) for {'C': 478.630092322638, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.500 (+/-0.000) for {'C': 478.630092322638, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.500 (+/-0.000) for {'C': 478.630092322638, 'kernel': 'rbf', 'gamma': 2.568029450667348e-07}
0.500 (+/-0.000) for {'C': 478.630092322638, 'kernel': 'rbf', 'gamma': 2.577507869970538e-07}
0.500 (+/-0.000) for {'C': 478.630092322638, 'kernel': 'rbf', 'gamma': 2.587021273464608e-07}
0.500 (+/-0.000) for {'C': 478.630092322638, 'kernel': 'rbf', 'gamma': 2.596569790273783e-07}
0.500 (+/-0.000) for {'C': 478.630092322638, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.500 (+/-0.000) for {'C': 4786.30092322638, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.500 (+/-0.000) for {'C': 4786.30092322638, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.500 (+/-0.000) for {'C': 4786.30092322638, 'kernel': 'rbf', 'gamma': 2.530463049461392e-07}
0.500 (+/-0.000) for {'C': 4786.30092322638, 'kernel': 'rbf', 'gamma': 2.539802813772814e-07}
0.500 (+/-0.000) for {'C': 4786.30092322638, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.500 (+/-0.000) for {'C': 4786.30092322638, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.500 (+/-0.000) for {'C': 4786.30092322638, 'kernel': 'rbf', 'gamma': 2.568029450667348e-07}
0.500 (+/-0.000) for {'C': 4786.30092322638, 'kernel': 'rbf', 'gamma': 2.577507869970538e-07}
0.500 (+/-0.000) for {'C': 4786.30092322638, 'kernel': 'rbf', 'gamma': 2.587021273464608e-07}
0.500 (+/-0.000) for {'C': 4786.30092322638, 'kernel': 'rbf', 'gamma': 2.596569790273783e-07}
0.500 (+/-0.000) for {'C': 4786.30092322638, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.617 (+/-0.238) for {'C': 47863.0092322638, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.617 (+/-0.238) for {'C': 47863.0092322638, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.617 (+/-0.238) for {'C': 47863.0092322638, 'kernel': 'rbf', 'gamma': 2.530463049461392e-07}
0.617 (+/-0.238) for {'C': 47863.0092322638, 'kernel': 'rbf', 'gamma': 2.539802813772814e-07}
0.617 (+/-0.238) for {'C': 47863.0092322638, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.617 (+/-0.238) for {'C': 47863.0092322638, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.617 (+/-0.238) for {'C': 47863.0092322638, 'kernel': 'rbf', 'gamma': 2.568029450667348e-07}
0.617 (+/-0.238) for {'C': 47863.0092322638, 'kernel': 'rbf', 'gamma': 2.577507869970538e-07}
0.617 (+/-0.238) for {'C': 47863.0092322638, 'kernel': 'rbf', 'gamma': 2.587021273464608e-07}
0.617 (+/-0.238) for {'C': 47863.0092322638, 'kernel': 'rbf', 'gamma': 2.596569790273783e-07}
0.617 (+/-0.238) for {'C': 47863.0092322638, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.667 (+/-0.316) for {'C': 478630.092322638, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.683 (+/-0.296) for {'C': 478630.092322638, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.667 (+/-0.316) for {'C': 478630.092322638, 'kernel': 'rbf', 'gamma': 2.530463049461392e-07}
0.667 (+/-0.316) for {'C': 478630.092322638, 'kernel': 'rbf', 'gamma': 2.539802813772814e-07}
0.667 (+/-0.316) for {'C': 478630.092322638, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.683 (+/-0.296) for {'C': 478630.092322638, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.667 (+/-0.316) for {'C': 478630.092322638, 'kernel': 'rbf', 'gamma': 2.568029450667348e-07}
0.667 (+/-0.316) for {'C': 478630.092322638, 'kernel': 'rbf', 'gamma': 2.577507869970538e-07}
0.667 (+/-0.316) for {'C': 478630.092322638, 'kernel': 'rbf', 'gamma': 2.587021273464608e-07}
0.667 (+/-0.316) for {'C': 478630.092322638, 'kernel': 'rbf', 'gamma': 2.596569790273783e-07}
0.667 (+/-0.316) for {'C': 478630.092322638, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.656 (+/-0.308) for {'C': 4786300.923226381, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.639 (+/-0.322) for {'C': 4786300.923226381, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.640 (+/-0.323) for {'C': 4786300.923226381, 'kernel': 'rbf', 'gamma': 2.530463049461392e-07}
0.640 (+/-0.323) for {'C': 4786300.923226381, 'kernel': 'rbf', 'gamma': 2.539802813772814e-07}
0.664 (+/-0.313) for {'C': 4786300.923226381, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.664 (+/-0.314) for {'C': 4786300.923226381, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.664 (+/-0.314) for {'C': 4786300.923226381, 'kernel': 'rbf', 'gamma': 2.568029450667348e-07}
0.664 (+/-0.313) for {'C': 4786300.923226381, 'kernel': 'rbf', 'gamma': 2.577507869970538e-07}
0.663 (+/-0.314) for {'C': 4786300.923226381, 'kernel': 'rbf', 'gamma': 2.587021273464608e-07}
0.664 (+/-0.314) for {'C': 4786300.923226381, 'kernel': 'rbf', 'gamma': 2.596569790273783e-07}
0.664 (+/-0.314) for {'C': 4786300.923226381, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.654 (+/-0.308) for {'C': 47863009.2322638, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.654 (+/-0.308) for {'C': 47863009.2322638, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.655 (+/-0.308) for {'C': 47863009.2322638, 'kernel': 'rbf', 'gamma': 2.530463049461392e-07}
0.638 (+/-0.323) for {'C': 47863009.2322638, 'kernel': 'rbf', 'gamma': 2.539802813772814e-07}
0.662 (+/-0.313) for {'C': 47863009.2322638, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.663 (+/-0.313) for {'C': 47863009.2322638, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.663 (+/-0.313) for {'C': 47863009.2322638, 'kernel': 'rbf', 'gamma': 2.568029450667348e-07}
0.663 (+/-0.313) for {'C': 47863009.2322638, 'kernel': 'rbf', 'gamma': 2.577507869970538e-07}
0.662 (+/-0.314) for {'C': 47863009.2322638, 'kernel': 'rbf', 'gamma': 2.587021273464608e-07}
0.662 (+/-0.314) for {'C': 47863009.2322638, 'kernel': 'rbf', 'gamma': 2.596569790273783e-07}
0.662 (+/-0.314) for {'C': 47863009.2322638, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.654 (+/-0.308) for {'C': 478630092.32263803, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.654 (+/-0.308) for {'C': 478630092.32263803, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.654 (+/-0.308) for {'C': 478630092.32263803, 'kernel': 'rbf', 'gamma': 2.530463049461392e-07}
0.638 (+/-0.323) for {'C': 478630092.32263803, 'kernel': 'rbf', 'gamma': 2.539802813772814e-07}
0.661 (+/-0.313) for {'C': 478630092.32263803, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.662 (+/-0.314) for {'C': 478630092.32263803, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.662 (+/-0.313) for {'C': 478630092.32263803, 'kernel': 'rbf', 'gamma': 2.568029450667348e-07}
0.663 (+/-0.312) for {'C': 478630092.32263803, 'kernel': 'rbf', 'gamma': 2.577507869970538e-07}
0.662 (+/-0.314) for {'C': 478630092.32263803, 'kernel': 'rbf', 'gamma': 2.587021273464608e-07}
0.662 (+/-0.314) for {'C': 478630092.32263803, 'kernel': 'rbf', 'gamma': 2.596569790273783e-07}
0.662 (+/-0.315) for {'C': 478630092.32263803, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.654 (+/-0.308) for {'C': 4786300923.22638, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.654 (+/-0.308) for {'C': 4786300923.22638, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.654 (+/-0.308) for {'C': 4786300923.22638, 'kernel': 'rbf', 'gamma': 2.530463049461392e-07}
0.638 (+/-0.323) for {'C': 4786300923.22638, 'kernel': 'rbf', 'gamma': 2.539802813772814e-07}
0.661 (+/-0.313) for {'C': 4786300923.22638, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.662 (+/-0.313) for {'C': 4786300923.22638, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.662 (+/-0.313) for {'C': 4786300923.22638, 'kernel': 'rbf', 'gamma': 2.568029450667348e-07}
0.663 (+/-0.312) for {'C': 4786300923.22638, 'kernel': 'rbf', 'gamma': 2.577507869970538e-07}
0.662 (+/-0.315) for {'C': 4786300923.22638, 'kernel': 'rbf', 'gamma': 2.587021273464608e-07}
0.662 (+/-0.314) for {'C': 4786300923.22638, 'kernel': 'rbf', 'gamma': 2.596569790273783e-07}
0.662 (+/-0.314) for {'C': 4786300923.22638, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.654 (+/-0.308) for {'C': 47863009232.2638, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.654 (+/-0.308) for {'C': 47863009232.2638, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
0.654 (+/-0.308) for {'C': 47863009232.2638, 'kernel': 'rbf', 'gamma': 2.530463049461392e-07}
0.638 (+/-0.323) for {'C': 47863009232.2638, 'kernel': 'rbf', 'gamma': 2.539802813772814e-07}
0.661 (+/-0.313) for {'C': 47863009232.2638, 'kernel': 'rbf', 'gamma': 2.54917705050912e-07}
0.662 (+/-0.313) for {'C': 47863009232.2638, 'kernel': 'rbf', 'gamma': 2.558585886905648e-07}
0.662 (+/-0.313) for {'C': 47863009232.2638, 'kernel': 'rbf', 'gamma': 2.568029450667348e-07}
0.663 (+/-0.312) for {'C': 47863009232.2638, 'kernel': 'rbf', 'gamma': 2.577507869970538e-07}
0.662 (+/-0.314) for {'C': 47863009232.2638, 'kernel': 'rbf', 'gamma': 2.587021273464608e-07}
0.662 (+/-0.314) for {'C': 47863009232.2638, 'kernel': 'rbf', 'gamma': 2.596569790273783e-07}
0.662 (+/-0.314) for {'C': 47863009232.2638, 'kernel': 'rbf', 'gamma': 2.6061535499988967e-07}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.663 (+/-0.316) for {'C': 10.0, 'kernel': 'linear'}
0.661 (+/-0.315) for {'C': 100.0, 'kernel': 'linear'}
0.637 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
0.611 (+/-0.241) for {'C': 10000.0, 'kernel': 'linear'}
0.636 (+/-0.237) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 478630.092322638, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6833333333333332
第2轮loop中,tunemodel函数得到的最优参数是: ([{'C': array([ 47863.00923226, 75857.75750292, 120226.44346174,
190546.07179632, 301995.1720402 , 478630.09232264,
758577.57502918, 1202264.43461741, 1905460.71796325,
3019951.72040201, 4786300.92322638]), 'kernel': ['rbf'], 'gamma': array([2.51188643e-07, 2.51373794e-07, 2.51559081e-07, 2.51744505e-07,
2.51930066e-07, 2.52115763e-07, 2.52301597e-07, 2.52487568e-07,
2.52673677e-07, 2.52859922e-07, 2.53046305e-07])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}], {'C': 478630.092322638, 'kernel': 'rbf', 'gamma': 2.5211576308074103e-07}, 0.6833333333333332)
这是第2次迭代微调C和gamma。
第2次迭代,得到delta: [4.65661287e-10 3.74282561e-09]
SVC模型clf_op_iter的参数是: {'kernel': 'rbf', 'decision_function_shape': 'ovr', 'class_weight': {-1: 15}, 'tol': 0.001, 'gamma': 2.5211576308074103e-07, 'random_state': 0, 'cache_size': 200, 'verbose': False, 'degree': 3, 'C': 478630.092322638, 'probability': False, 'shrinking': True, 'coef0': 0.0, 'max_iter': -1}
训练模型SVC对训练样本的预测准确率: 0.9362154500354358
测试集中,预测为舞弊样本的有: (array([ 370, 658, 1246, 1247, 1248, 1255, 1256], dtype=int64),)
测试集中,实际为舞弊样本的有: (array([1246, 1247, 1248, 1249, 1250, 1251, 1252, 1253, 1254, 1255, 1256],
dtype=int64),)
预测的舞弊样本数目: 7
训练模型SVC对测试样本的预测准确率: 0.9347980155917789
以上是第29次特征筛选。
第29次特征筛选,AUC值是: 0.7264701590544287
X_train_iter_svc.shape is: (1257, 23)
X_test_iter_svc.shape is: (1257, 23)
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.647 (+/-0.401) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.747 (+/-0.449) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.17 0.17 0.17 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.589 (+/-0.232) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.641 (+/-0.236) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.17 0.17 0.17 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6413461538461539
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.748 (+/-0.449) for {'C': 1.0, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.653 (+/-0.293) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.20 0.17 0.18 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.662 (+/-0.227) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.20 0.17 0.18 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.99 0.99 629
本轮grid search结果,得到最好的参数选择是: {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6663461538461538
RBF的粗grid search最佳超参: {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001} RBF的粗grid search最高分值: 0.6413461538461539
linear的粗grid search最佳超参: {'C': 1.0, 'kernel': 'linear'} linear的粗grid search最高分值: 0.6663461538461538
粗grid search得到的parameter是:
[{'C': array([1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02, 1.e+03, 1.e+04, 1.e+05,
1.e+06, 1.e+07, 1.e+08]), 'kernel': ['rbf'], 'gamma': array([1.e-08, 1.e-07, 1.e-06, 1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01,
1.e+00, 1.e+01, 1.e+02])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.647 (+/-0.333) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.449) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.449) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.375) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.647 (+/-0.401) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.449) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.706 (+/-0.383) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.629 (+/-0.312) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.647 (+/-0.401) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.747 (+/-0.502) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.748 (+/-0.449) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.706 (+/-0.383) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.625 (+/-0.314) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.647 (+/-0.401) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.747 (+/-0.502) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.748 (+/-0.449) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.723 (+/-0.417) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.642 (+/-0.299) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.621 (+/-0.320) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.647 (+/-0.401) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.649 (+/-0.778) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.797 (+/-0.492) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.656 (+/-0.284) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.630 (+/-0.312) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.638 (+/-0.302) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.619 (+/-0.322) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.647 (+/-0.401) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.699 (+/-0.797) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.797 (+/-0.492) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.659 (+/-0.313) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.626 (+/-0.313) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.620 (+/-0.321) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.619 (+/-0.322) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.647 (+/-0.401) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'linear'}
0.706 (+/-0.383) for {'C': 10.0, 'kernel': 'linear'}
0.643 (+/-0.320) for {'C': 100.0, 'kernel': 'linear'}
0.653 (+/-0.293) for {'C': 1000.0, 'kernel': 'linear'}
0.619 (+/-0.322) for {'C': 10000.0, 'kernel': 'linear'}
0.619 (+/-0.322) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.006) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.004) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.641 (+/-0.236) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.240) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.004) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.641 (+/-0.236) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.641 (+/-0.236) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.587 (+/-0.233) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.589 (+/-0.232) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.004) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.641 (+/-0.236) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.666 (+/-0.316) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.637 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.588 (+/-0.232) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.589 (+/-0.232) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.004) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.617 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.666 (+/-0.316) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.316) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.636 (+/-0.236) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.588 (+/-0.233) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.588 (+/-0.232) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.589 (+/-0.232) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.004) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.617 (+/-0.238) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.666 (+/-0.316) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.666 (+/-0.317) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.662 (+/-0.225) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.612 (+/-0.241) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.587 (+/-0.234) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.588 (+/-0.232) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.589 (+/-0.232) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.004) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.617 (+/-0.238) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.642 (+/-0.236) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.681 (+/-0.294) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.658 (+/-0.320) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.661 (+/-0.223) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.611 (+/-0.240) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.587 (+/-0.234) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.588 (+/-0.232) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.589 (+/-0.232) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.004) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.642 (+/-0.236) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.642 (+/-0.236) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.664 (+/-0.316) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.658 (+/-0.316) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.611 (+/-0.242) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.611 (+/-0.240) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.587 (+/-0.234) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.588 (+/-0.232) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.589 (+/-0.232) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.004) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'linear'}
0.666 (+/-0.315) for {'C': 10.0, 'kernel': 'linear'}
0.637 (+/-0.237) for {'C': 100.0, 'kernel': 'linear'}
0.662 (+/-0.227) for {'C': 1000.0, 'kernel': 'linear'}
0.611 (+/-0.241) for {'C': 10000.0, 'kernel': 'linear'}
0.611 (+/-0.240) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.10 0.17 0.12 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6807682001813835
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.673 (+/-0.330) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.627 (+/-0.330) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.714 (+/-0.359) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.609 (+/-0.313) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.389) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.714 (+/-0.359) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.658 (+/-0.387) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.647 (+/-0.401) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.714 (+/-0.359) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.658 (+/-0.387) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.629 (+/-0.312) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.647 (+/-0.401) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.747 (+/-0.502) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.797 (+/-0.492) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.652 (+/-0.313) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.658 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.625 (+/-0.314) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.647 (+/-0.401) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.024) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.747 (+/-0.502) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.656 (+/-0.291) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.658 (+/-0.388) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.642 (+/-0.299) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.621 (+/-0.320) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.647 (+/-0.401) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.699 (+/-0.797) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.681 (+/-0.372) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.629 (+/-0.283) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.626 (+/-0.314) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.638 (+/-0.302) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.619 (+/-0.322) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.647 (+/-0.401) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.654 (+/-0.775) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.677 (+/-0.374) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.624 (+/-0.315) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.626 (+/-0.314) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.620 (+/-0.321) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.619 (+/-0.322) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.647 (+/-0.401) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 1.0, 'kernel': 'linear'}
0.673 (+/-0.377) for {'C': 10.0, 'kernel': 'linear'}
0.631 (+/-0.312) for {'C': 100.0, 'kernel': 'linear'}
0.653 (+/-0.293) for {'C': 1000.0, 'kernel': 'linear'}
0.619 (+/-0.322) for {'C': 10000.0, 'kernel': 'linear'}
0.619 (+/-0.322) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.006) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.666 (+/-0.315) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.611 (+/-0.241) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.004) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.682 (+/-0.295) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.609 (+/-0.242) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.240) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.004) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.682 (+/-0.295) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.658 (+/-0.314) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.587 (+/-0.233) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.589 (+/-0.232) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.004) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.682 (+/-0.295) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.658 (+/-0.314) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.637 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.588 (+/-0.232) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.589 (+/-0.232) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.004) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.617 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.642 (+/-0.236) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.665 (+/-0.314) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.658 (+/-0.314) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.636 (+/-0.236) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.588 (+/-0.233) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.588 (+/-0.232) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.589 (+/-0.232) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.004) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.523 (+/-0.138) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.617 (+/-0.238) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.681 (+/-0.292) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.658 (+/-0.314) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.662 (+/-0.225) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.612 (+/-0.241) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.587 (+/-0.234) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.588 (+/-0.232) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.589 (+/-0.232) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.004) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.642 (+/-0.236) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.641 (+/-0.235) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.676 (+/-0.295) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.657 (+/-0.319) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.661 (+/-0.223) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.611 (+/-0.240) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.587 (+/-0.234) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.588 (+/-0.232) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.589 (+/-0.232) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.004) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.640 (+/-0.234) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.641 (+/-0.234) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.657 (+/-0.315) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.658 (+/-0.316) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.611 (+/-0.242) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.611 (+/-0.240) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.587 (+/-0.234) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.588 (+/-0.232) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.589 (+/-0.232) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.004) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.663 (+/-0.315) for {'C': 10.0, 'kernel': 'linear'}
0.661 (+/-0.315) for {'C': 100.0, 'kernel': 'linear'}
0.662 (+/-0.227) for {'C': 1000.0, 'kernel': 'linear'}
0.611 (+/-0.241) for {'C': 10000.0, 'kernel': 'linear'}
0.611 (+/-0.240) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.17 0.17 0.17 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6822115384615385
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.496 (+/-0.001) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.747 (+/-0.502) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.747 (+/-0.502) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.747 (+/-0.502) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.747 (+/-0.502) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.747 (+/-0.502) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.747 (+/-0.502) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.747 (+/-0.502) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.747 (+/-0.502) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.697 (+/-0.437) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.747 (+/-0.502) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.747 (+/-0.502) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.747 (+/-0.502) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.747 (+/-0.502) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.747 (+/-0.502) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.747 (+/-0.502) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.747 (+/-0.502) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.747 (+/-0.449) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.689 (+/-0.436) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.747 (+/-0.502) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.747 (+/-0.502) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.747 (+/-0.502) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.747 (+/-0.502) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.747 (+/-0.502) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.747 (+/-0.502) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.747 (+/-0.449) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.723 (+/-0.423) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.664 (+/-0.388) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.747 (+/-0.502) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.747 (+/-0.502) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.747 (+/-0.502) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.747 (+/-0.502) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.747 (+/-0.502) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.747 (+/-0.449) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.723 (+/-0.423) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.664 (+/-0.388) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.631 (+/-0.319) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.747 (+/-0.502) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.747 (+/-0.502) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.747 (+/-0.502) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.747 (+/-0.502) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.747 (+/-0.449) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.723 (+/-0.423) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.698 (+/-0.383) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.664 (+/-0.388) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.664 (+/-0.388) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.747 (+/-0.502) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.747 (+/-0.502) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.747 (+/-0.502) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.747 (+/-0.449) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.723 (+/-0.423) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.723 (+/-0.423) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.664 (+/-0.388) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.672 (+/-0.392) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.631 (+/-0.319) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.747 (+/-0.502) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.722 (+/-0.473) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.747 (+/-0.449) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.723 (+/-0.423) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.723 (+/-0.423) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.689 (+/-0.374) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.672 (+/-0.392) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.664 (+/-0.388) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.626 (+/-0.318) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.722 (+/-0.473) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.747 (+/-0.449) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.723 (+/-0.423) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.723 (+/-0.423) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.698 (+/-0.383) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.697 (+/-0.375) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.672 (+/-0.392) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.614 (+/-0.327) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.614 (+/-0.327) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.001}
0.722 (+/-0.473) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.747 (+/-0.449) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.723 (+/-0.423) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.723 (+/-0.423) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.698 (+/-0.383) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.697 (+/-0.375) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.672 (+/-0.392) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.622 (+/-0.336) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.614 (+/-0.327) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.614 (+/-0.327) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.449) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.723 (+/-0.423) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.723 (+/-0.423) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.698 (+/-0.383) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.697 (+/-0.375) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.672 (+/-0.392) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.657 (+/-0.390) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.614 (+/-0.327) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.614 (+/-0.327) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.614 (+/-0.327) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.449) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.723 (+/-0.423) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.723 (+/-0.423) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.698 (+/-0.383) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.706 (+/-0.383) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.697 (+/-0.375) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.656 (+/-0.392) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.621 (+/-0.320) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.618 (+/-0.322) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.614 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.614 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'linear'}
0.706 (+/-0.383) for {'C': 10.0, 'kernel': 'linear'}
0.643 (+/-0.320) for {'C': 100.0, 'kernel': 'linear'}
0.653 (+/-0.293) for {'C': 1000.0, 'kernel': 'linear'}
0.619 (+/-0.322) for {'C': 10000.0, 'kernel': 'linear'}
0.619 (+/-0.322) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.25 0.17 0.20 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.500 (+/-0.000) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.617 (+/-0.238) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.617 (+/-0.238) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.617 (+/-0.238) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.617 (+/-0.238) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.617 (+/-0.238) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.617 (+/-0.238) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.617 (+/-0.238) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.617 (+/-0.238) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.616 (+/-0.237) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.617 (+/-0.238) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.617 (+/-0.238) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.617 (+/-0.238) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.617 (+/-0.238) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.617 (+/-0.238) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.617 (+/-0.238) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.617 (+/-0.238) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.641 (+/-0.236) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.616 (+/-0.237) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.617 (+/-0.238) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.617 (+/-0.238) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.617 (+/-0.238) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.617 (+/-0.238) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.617 (+/-0.238) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.617 (+/-0.238) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.641 (+/-0.236) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.666 (+/-0.315) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.616 (+/-0.237) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.617 (+/-0.238) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.617 (+/-0.238) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.617 (+/-0.238) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.617 (+/-0.238) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.617 (+/-0.238) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.641 (+/-0.236) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.666 (+/-0.315) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.616 (+/-0.237) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.616 (+/-0.236) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.617 (+/-0.238) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.617 (+/-0.238) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.617 (+/-0.238) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.617 (+/-0.238) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.641 (+/-0.236) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.666 (+/-0.315) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.666 (+/-0.315) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.616 (+/-0.237) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.616 (+/-0.237) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.617 (+/-0.238) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.617 (+/-0.238) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.617 (+/-0.238) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.641 (+/-0.236) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.666 (+/-0.315) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.666 (+/-0.315) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.616 (+/-0.237) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.616 (+/-0.238) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.615 (+/-0.237) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.617 (+/-0.238) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.617 (+/-0.238) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.641 (+/-0.236) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.666 (+/-0.315) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.666 (+/-0.315) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.641 (+/-0.235) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.616 (+/-0.238) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.616 (+/-0.238) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.614 (+/-0.238) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.617 (+/-0.238) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.641 (+/-0.236) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.666 (+/-0.315) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.666 (+/-0.315) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.666 (+/-0.315) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.641 (+/-0.236) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.616 (+/-0.238) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.589 (+/-0.230) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.587 (+/-0.233) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.641 (+/-0.236) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.666 (+/-0.315) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.666 (+/-0.315) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.666 (+/-0.315) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.641 (+/-0.236) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.616 (+/-0.238) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.589 (+/-0.231) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.588 (+/-0.232) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.587 (+/-0.234) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.001}
0.641 (+/-0.236) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.666 (+/-0.315) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.666 (+/-0.315) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.666 (+/-0.315) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.641 (+/-0.236) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.616 (+/-0.238) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.615 (+/-0.238) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.588 (+/-0.232) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.587 (+/-0.234) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.587 (+/-0.234) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.1}
0.641 (+/-0.236) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.315) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.666 (+/-0.315) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.666 (+/-0.315) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.666 (+/-0.315) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.641 (+/-0.236) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.614 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.613 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.612 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.587 (+/-0.234) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.587 (+/-0.233) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'linear'}
0.666 (+/-0.315) for {'C': 10.0, 'kernel': 'linear'}
0.637 (+/-0.237) for {'C': 100.0, 'kernel': 'linear'}
0.662 (+/-0.227) for {'C': 1000.0, 'kernel': 'linear'}
0.611 (+/-0.241) for {'C': 10000.0, 'kernel': 'linear'}
0.611 (+/-0.240) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.20 0.17 0.18 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.99 0.99 629
本轮grid search结果,得到最好的参数选择是: {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.666025641025641
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.747 (+/-0.502) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.747 (+/-0.502) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.747 (+/-0.502) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.747 (+/-0.502) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.747 (+/-0.502) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.747 (+/-0.502) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.797 (+/-0.492) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.748 (+/-0.449) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.689 (+/-0.382) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.673 (+/-0.330) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.747 (+/-0.502) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.747 (+/-0.502) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.747 (+/-0.502) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.747 (+/-0.502) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.797 (+/-0.492) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.748 (+/-0.449) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.698 (+/-0.383) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.664 (+/-0.326) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.673 (+/-0.330) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.747 (+/-0.502) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.747 (+/-0.502) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.747 (+/-0.502) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.797 (+/-0.492) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.748 (+/-0.449) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.706 (+/-0.383) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.664 (+/-0.326) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.673 (+/-0.330) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.651 (+/-0.308) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.747 (+/-0.502) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.747 (+/-0.502) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.797 (+/-0.492) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.748 (+/-0.449) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.706 (+/-0.383) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.664 (+/-0.326) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.673 (+/-0.330) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.651 (+/-0.308) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.626 (+/-0.313) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.747 (+/-0.502) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.797 (+/-0.492) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.748 (+/-0.449) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.706 (+/-0.383) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.714 (+/-0.359) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.673 (+/-0.330) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.651 (+/-0.308) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.659 (+/-0.385) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.645 (+/-0.388) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.797 (+/-0.492) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.748 (+/-0.449) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.706 (+/-0.383) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.714 (+/-0.359) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.673 (+/-0.330) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.689 (+/-0.374) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.669 (+/-0.373) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.659 (+/-0.390) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.609 (+/-0.313) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.001}
0.797 (+/-0.492) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.748 (+/-0.449) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.706 (+/-0.383) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.714 (+/-0.359) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.673 (+/-0.330) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.689 (+/-0.374) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.669 (+/-0.373) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.663 (+/-0.383) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.647 (+/-0.387) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.618 (+/-0.322) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.1}
0.797 (+/-0.492) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.001}
0.748 (+/-0.449) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.706 (+/-0.383) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.714 (+/-0.359) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.673 (+/-0.330) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.689 (+/-0.374) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.669 (+/-0.373) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.662 (+/-0.384) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.659 (+/-0.387) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.622 (+/-0.319) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.614 (+/-0.327) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.1}
0.748 (+/-0.449) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.001}
0.706 (+/-0.383) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.714 (+/-0.359) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.673 (+/-0.330) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.689 (+/-0.374) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.669 (+/-0.373) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.662 (+/-0.384) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.658 (+/-0.387) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.630 (+/-0.328) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.618 (+/-0.322) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.614 (+/-0.327) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.1}
0.706 (+/-0.383) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.001}
0.714 (+/-0.359) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.673 (+/-0.330) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.689 (+/-0.374) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.669 (+/-0.373) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.662 (+/-0.384) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.658 (+/-0.387) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.655 (+/-0.392) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.622 (+/-0.319) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.614 (+/-0.327) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.614 (+/-0.327) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.1}
0.714 (+/-0.359) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.673 (+/-0.330) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.689 (+/-0.374) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.669 (+/-0.373) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.663 (+/-0.384) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.658 (+/-0.387) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.660 (+/-0.385) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.627 (+/-0.313) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.625 (+/-0.314) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.614 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.614 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 1.0, 'kernel': 'linear'}
0.673 (+/-0.377) for {'C': 10.0, 'kernel': 'linear'}
0.631 (+/-0.312) for {'C': 100.0, 'kernel': 'linear'}
0.653 (+/-0.293) for {'C': 1000.0, 'kernel': 'linear'}
0.619 (+/-0.322) for {'C': 10000.0, 'kernel': 'linear'}
0.619 (+/-0.322) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.617 (+/-0.238) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.617 (+/-0.238) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.617 (+/-0.238) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.617 (+/-0.238) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.617 (+/-0.238) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.617 (+/-0.238) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.642 (+/-0.236) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.666 (+/-0.316) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.666 (+/-0.315) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.666 (+/-0.315) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.617 (+/-0.238) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.617 (+/-0.238) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.617 (+/-0.238) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.617 (+/-0.238) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.642 (+/-0.236) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.666 (+/-0.316) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.666 (+/-0.315) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.666 (+/-0.315) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.665 (+/-0.315) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.617 (+/-0.238) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.617 (+/-0.238) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.617 (+/-0.238) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.642 (+/-0.236) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.666 (+/-0.316) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.666 (+/-0.315) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.666 (+/-0.315) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.665 (+/-0.315) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.665 (+/-0.314) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.617 (+/-0.238) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.617 (+/-0.238) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.642 (+/-0.236) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.666 (+/-0.316) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.666 (+/-0.315) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.666 (+/-0.315) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.665 (+/-0.315) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.665 (+/-0.314) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.639 (+/-0.325) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.617 (+/-0.238) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.642 (+/-0.236) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.666 (+/-0.316) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.666 (+/-0.315) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.682 (+/-0.295) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.665 (+/-0.315) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.665 (+/-0.314) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.639 (+/-0.325) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.611 (+/-0.242) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.642 (+/-0.236) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.666 (+/-0.316) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.666 (+/-0.315) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.682 (+/-0.295) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.665 (+/-0.315) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.665 (+/-0.315) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.664 (+/-0.315) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.636 (+/-0.327) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.609 (+/-0.242) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.001}
0.642 (+/-0.236) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.666 (+/-0.316) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.666 (+/-0.315) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.682 (+/-0.295) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.665 (+/-0.315) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.665 (+/-0.315) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.664 (+/-0.315) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.661 (+/-0.315) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.634 (+/-0.327) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.609 (+/-0.242) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.1}
0.642 (+/-0.236) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.316) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.666 (+/-0.315) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.682 (+/-0.295) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.665 (+/-0.315) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.665 (+/-0.315) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.664 (+/-0.315) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.661 (+/-0.314) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.659 (+/-0.314) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.635 (+/-0.325) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.585 (+/-0.237) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.1}
0.666 (+/-0.316) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.315) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.682 (+/-0.295) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.665 (+/-0.315) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.665 (+/-0.315) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.664 (+/-0.315) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.661 (+/-0.314) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.658 (+/-0.314) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.635 (+/-0.325) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.610 (+/-0.242) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.586 (+/-0.236) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.1}
0.666 (+/-0.315) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.001}
0.682 (+/-0.295) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.665 (+/-0.315) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.665 (+/-0.315) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.664 (+/-0.315) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.661 (+/-0.314) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.658 (+/-0.314) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.635 (+/-0.325) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.635 (+/-0.324) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.586 (+/-0.236) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.587 (+/-0.234) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.1}
0.682 (+/-0.295) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.665 (+/-0.315) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.665 (+/-0.315) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.664 (+/-0.315) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.661 (+/-0.315) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.658 (+/-0.314) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.660 (+/-0.313) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.660 (+/-0.313) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.636 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.587 (+/-0.235) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.587 (+/-0.233) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.663 (+/-0.315) for {'C': 10.0, 'kernel': 'linear'}
0.661 (+/-0.315) for {'C': 100.0, 'kernel': 'linear'}
0.662 (+/-0.227) for {'C': 1000.0, 'kernel': 'linear'}
0.611 (+/-0.241) for {'C': 10000.0, 'kernel': 'linear'}
0.611 (+/-0.240) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.17 0.17 0.17 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6822115384615385
循环迭代之前,delta is: [3.69042656e+01 5.84893192e-03]
执行tunemodel()函数前,使用的grid search parameter是:
[{'C': array([ 39.81071706, 43.65158322, 47.86300923, 52.48074602,
57.54399373, 63.09573445, 69.18309709, 75.8577575 ,
83.17637711, 91.20108394, 100. ]), 'kernel': ['rbf'], 'gamma': array([0.01 , 0.01096478, 0.01202264, 0.01318257, 0.0144544 ,
0.01584893, 0.01737801, 0.01905461, 0.02089296, 0.02290868,
0.02511886])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.747 (+/-0.502) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.747 (+/-0.502) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.747 (+/-0.502) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.747 (+/-0.502) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.747 (+/-0.502) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.747 (+/-0.502) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.722 (+/-0.473) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.747 (+/-0.449) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.747 (+/-0.449) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.747 (+/-0.449) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.747 (+/-0.449) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.747 (+/-0.502) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.747 (+/-0.502) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.747 (+/-0.502) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.747 (+/-0.502) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.747 (+/-0.502) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.722 (+/-0.473) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.747 (+/-0.449) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.747 (+/-0.449) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.747 (+/-0.449) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.747 (+/-0.449) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.747 (+/-0.449) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.747 (+/-0.502) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.747 (+/-0.502) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.747 (+/-0.502) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.747 (+/-0.502) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.722 (+/-0.473) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.747 (+/-0.449) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.747 (+/-0.449) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.747 (+/-0.449) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.747 (+/-0.449) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.747 (+/-0.449) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.748 (+/-0.449) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.747 (+/-0.502) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.747 (+/-0.502) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.747 (+/-0.502) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.722 (+/-0.473) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.747 (+/-0.449) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.747 (+/-0.449) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.747 (+/-0.449) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.747 (+/-0.449) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.747 (+/-0.449) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.748 (+/-0.449) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.748 (+/-0.449) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.747 (+/-0.502) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.747 (+/-0.502) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.722 (+/-0.473) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.747 (+/-0.449) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.747 (+/-0.449) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.747 (+/-0.449) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.747 (+/-0.449) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.747 (+/-0.449) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.748 (+/-0.449) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.748 (+/-0.449) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.748 (+/-0.449) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.747 (+/-0.502) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.722 (+/-0.473) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.747 (+/-0.449) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.747 (+/-0.449) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.747 (+/-0.449) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.747 (+/-0.449) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.747 (+/-0.449) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.748 (+/-0.449) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.748 (+/-0.449) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.748 (+/-0.449) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.723 (+/-0.423) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.722 (+/-0.473) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.747 (+/-0.449) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.747 (+/-0.449) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.747 (+/-0.449) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.747 (+/-0.449) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.747 (+/-0.449) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.748 (+/-0.449) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.748 (+/-0.449) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.748 (+/-0.449) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.723 (+/-0.423) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.723 (+/-0.423) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.747 (+/-0.449) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.747 (+/-0.449) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.747 (+/-0.449) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.747 (+/-0.449) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.747 (+/-0.449) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.748 (+/-0.449) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.748 (+/-0.449) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.748 (+/-0.449) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.723 (+/-0.423) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.723 (+/-0.423) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.698 (+/-0.383) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.747 (+/-0.449) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.747 (+/-0.449) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.747 (+/-0.449) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.747 (+/-0.449) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.748 (+/-0.449) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.748 (+/-0.449) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.748 (+/-0.449) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.723 (+/-0.423) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.723 (+/-0.423) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.698 (+/-0.383) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.698 (+/-0.383) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.747 (+/-0.449) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.747 (+/-0.449) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.748 (+/-0.449) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.748 (+/-0.449) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.748 (+/-0.449) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.748 (+/-0.449) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.723 (+/-0.423) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.723 (+/-0.423) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.698 (+/-0.383) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.698 (+/-0.383) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.698 (+/-0.383) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.747 (+/-0.449) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.748 (+/-0.449) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.748 (+/-0.449) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.748 (+/-0.449) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.748 (+/-0.449) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.723 (+/-0.423) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.723 (+/-0.423) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.698 (+/-0.383) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.698 (+/-0.383) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.723 (+/-0.423) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.723 (+/-0.423) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'linear'}
0.706 (+/-0.383) for {'C': 10.0, 'kernel': 'linear'}
0.643 (+/-0.320) for {'C': 100.0, 'kernel': 'linear'}
0.653 (+/-0.293) for {'C': 1000.0, 'kernel': 'linear'}
0.619 (+/-0.322) for {'C': 10000.0, 'kernel': 'linear'}
0.619 (+/-0.322) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.20 0.17 0.18 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.617 (+/-0.238) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.617 (+/-0.238) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.617 (+/-0.238) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.617 (+/-0.238) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.617 (+/-0.238) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.617 (+/-0.238) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.617 (+/-0.238) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.641 (+/-0.236) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.641 (+/-0.236) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.641 (+/-0.236) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.641 (+/-0.236) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.617 (+/-0.238) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.617 (+/-0.238) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.617 (+/-0.238) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.617 (+/-0.238) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.617 (+/-0.238) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.617 (+/-0.238) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.641 (+/-0.236) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.641 (+/-0.236) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.641 (+/-0.236) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.641 (+/-0.236) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.641 (+/-0.236) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.617 (+/-0.238) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.617 (+/-0.238) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.617 (+/-0.238) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.617 (+/-0.238) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.617 (+/-0.238) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.641 (+/-0.236) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.641 (+/-0.236) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.641 (+/-0.236) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.641 (+/-0.236) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.641 (+/-0.236) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.666 (+/-0.316) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.617 (+/-0.238) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.617 (+/-0.238) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.617 (+/-0.238) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.617 (+/-0.238) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.641 (+/-0.236) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.641 (+/-0.236) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.641 (+/-0.236) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.641 (+/-0.236) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.641 (+/-0.236) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.666 (+/-0.316) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.666 (+/-0.316) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.617 (+/-0.238) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.617 (+/-0.238) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.617 (+/-0.238) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.641 (+/-0.236) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.641 (+/-0.236) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.641 (+/-0.236) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.641 (+/-0.236) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.641 (+/-0.236) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.666 (+/-0.316) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.666 (+/-0.316) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.666 (+/-0.316) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.617 (+/-0.238) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.617 (+/-0.238) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.641 (+/-0.236) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.641 (+/-0.236) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.641 (+/-0.236) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.641 (+/-0.236) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.641 (+/-0.236) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.666 (+/-0.316) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.666 (+/-0.316) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.666 (+/-0.316) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.666 (+/-0.315) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.617 (+/-0.238) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.641 (+/-0.236) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.641 (+/-0.236) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.641 (+/-0.236) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.641 (+/-0.236) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.641 (+/-0.236) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.666 (+/-0.316) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.666 (+/-0.316) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.666 (+/-0.316) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.666 (+/-0.315) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.666 (+/-0.315) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.641 (+/-0.236) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.641 (+/-0.236) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.641 (+/-0.236) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.641 (+/-0.236) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.641 (+/-0.236) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.666 (+/-0.316) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.666 (+/-0.316) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.666 (+/-0.316) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.666 (+/-0.315) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.666 (+/-0.315) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.666 (+/-0.315) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.641 (+/-0.236) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.641 (+/-0.236) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.641 (+/-0.236) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.641 (+/-0.236) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.666 (+/-0.316) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.666 (+/-0.316) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.666 (+/-0.316) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.666 (+/-0.315) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.666 (+/-0.315) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.666 (+/-0.315) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.666 (+/-0.315) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.641 (+/-0.236) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.641 (+/-0.236) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.666 (+/-0.316) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.666 (+/-0.316) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.666 (+/-0.316) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.666 (+/-0.316) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.666 (+/-0.315) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.666 (+/-0.315) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.666 (+/-0.315) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.666 (+/-0.315) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.666 (+/-0.315) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.641 (+/-0.236) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.666 (+/-0.316) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.666 (+/-0.316) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.666 (+/-0.316) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.666 (+/-0.316) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.666 (+/-0.315) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.666 (+/-0.315) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.666 (+/-0.315) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.666 (+/-0.315) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.666 (+/-0.315) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.666 (+/-0.315) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'linear'}
0.666 (+/-0.315) for {'C': 10.0, 'kernel': 'linear'}
0.637 (+/-0.237) for {'C': 100.0, 'kernel': 'linear'}
0.662 (+/-0.227) for {'C': 1000.0, 'kernel': 'linear'}
0.611 (+/-0.241) for {'C': 10000.0, 'kernel': 'linear'}
0.611 (+/-0.240) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.20 0.17 0.18 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.99 0.99 629
本轮grid search结果,得到最好的参数选择是: {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6663461538461538
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.748 (+/-0.449) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.748 (+/-0.449) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.748 (+/-0.449) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.748 (+/-0.449) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.748 (+/-0.449) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.706 (+/-0.383) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.685 (+/-0.384) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.677 (+/-0.374) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.727 (+/-0.397) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.710 (+/-0.363) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.664 (+/-0.326) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.748 (+/-0.449) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.748 (+/-0.449) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.748 (+/-0.449) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.748 (+/-0.449) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.706 (+/-0.383) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.685 (+/-0.384) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.677 (+/-0.374) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.727 (+/-0.397) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.710 (+/-0.363) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.664 (+/-0.326) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.664 (+/-0.326) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.748 (+/-0.449) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.748 (+/-0.449) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.748 (+/-0.449) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.706 (+/-0.383) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.685 (+/-0.384) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.677 (+/-0.374) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.727 (+/-0.397) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.710 (+/-0.363) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.664 (+/-0.326) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.664 (+/-0.326) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.673 (+/-0.330) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.748 (+/-0.449) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.748 (+/-0.449) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.706 (+/-0.383) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.685 (+/-0.384) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.677 (+/-0.374) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.727 (+/-0.397) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.710 (+/-0.363) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.664 (+/-0.326) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.664 (+/-0.326) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.673 (+/-0.330) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.673 (+/-0.330) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.748 (+/-0.449) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.706 (+/-0.383) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.685 (+/-0.384) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.677 (+/-0.374) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.727 (+/-0.397) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.710 (+/-0.363) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.714 (+/-0.359) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.664 (+/-0.326) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.673 (+/-0.330) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.673 (+/-0.330) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.673 (+/-0.330) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.706 (+/-0.383) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.685 (+/-0.384) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.677 (+/-0.374) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.727 (+/-0.397) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.710 (+/-0.363) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.714 (+/-0.359) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.714 (+/-0.359) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.673 (+/-0.330) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.673 (+/-0.330) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.673 (+/-0.330) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.673 (+/-0.330) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.685 (+/-0.384) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.677 (+/-0.374) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.727 (+/-0.397) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.710 (+/-0.363) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.714 (+/-0.359) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.714 (+/-0.359) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.673 (+/-0.330) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.673 (+/-0.330) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.673 (+/-0.330) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.673 (+/-0.330) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.673 (+/-0.330) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.677 (+/-0.374) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.727 (+/-0.397) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.710 (+/-0.363) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.714 (+/-0.359) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.714 (+/-0.359) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.673 (+/-0.330) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.673 (+/-0.330) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.673 (+/-0.330) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.673 (+/-0.330) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.673 (+/-0.330) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.673 (+/-0.330) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.727 (+/-0.397) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.710 (+/-0.363) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.714 (+/-0.359) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.714 (+/-0.359) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.673 (+/-0.330) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.673 (+/-0.330) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.673 (+/-0.330) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.673 (+/-0.330) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.673 (+/-0.330) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.673 (+/-0.330) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.664 (+/-0.317) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.710 (+/-0.363) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.714 (+/-0.359) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.714 (+/-0.359) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.673 (+/-0.330) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.673 (+/-0.330) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.673 (+/-0.330) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.673 (+/-0.330) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.673 (+/-0.330) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.673 (+/-0.330) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.664 (+/-0.317) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.664 (+/-0.317) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.714 (+/-0.359) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.714 (+/-0.359) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.673 (+/-0.330) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.673 (+/-0.330) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.673 (+/-0.330) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.673 (+/-0.330) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.673 (+/-0.330) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.673 (+/-0.330) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.664 (+/-0.317) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.689 (+/-0.374) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.689 (+/-0.374) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 1.0, 'kernel': 'linear'}
0.673 (+/-0.377) for {'C': 10.0, 'kernel': 'linear'}
0.631 (+/-0.312) for {'C': 100.0, 'kernel': 'linear'}
0.653 (+/-0.293) for {'C': 1000.0, 'kernel': 'linear'}
0.619 (+/-0.322) for {'C': 10000.0, 'kernel': 'linear'}
0.619 (+/-0.322) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.20 0.17 0.18 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.666 (+/-0.316) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.666 (+/-0.316) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.666 (+/-0.316) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.666 (+/-0.316) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.666 (+/-0.316) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.666 (+/-0.315) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.666 (+/-0.315) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.665 (+/-0.314) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.682 (+/-0.294) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.682 (+/-0.295) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.666 (+/-0.315) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.666 (+/-0.316) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.666 (+/-0.316) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.666 (+/-0.316) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.666 (+/-0.316) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.666 (+/-0.315) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.666 (+/-0.315) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.665 (+/-0.314) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.682 (+/-0.294) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.682 (+/-0.295) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.666 (+/-0.315) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.666 (+/-0.315) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.666 (+/-0.316) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.666 (+/-0.316) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.666 (+/-0.316) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.666 (+/-0.315) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.666 (+/-0.315) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.665 (+/-0.314) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.682 (+/-0.294) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.682 (+/-0.295) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.666 (+/-0.315) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.666 (+/-0.315) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.666 (+/-0.315) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.666 (+/-0.316) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.666 (+/-0.316) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.666 (+/-0.315) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.666 (+/-0.315) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.665 (+/-0.314) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.682 (+/-0.294) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.682 (+/-0.295) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.666 (+/-0.315) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.666 (+/-0.315) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.666 (+/-0.315) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.666 (+/-0.315) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.666 (+/-0.316) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.666 (+/-0.315) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.666 (+/-0.315) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.665 (+/-0.314) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.682 (+/-0.294) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.682 (+/-0.295) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.682 (+/-0.295) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.666 (+/-0.315) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.666 (+/-0.315) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.666 (+/-0.315) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.666 (+/-0.315) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.666 (+/-0.315) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.666 (+/-0.315) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.665 (+/-0.314) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.682 (+/-0.294) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.682 (+/-0.295) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.682 (+/-0.295) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.682 (+/-0.295) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.666 (+/-0.315) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.666 (+/-0.315) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.666 (+/-0.315) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.665 (+/-0.315) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.666 (+/-0.315) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.665 (+/-0.314) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.682 (+/-0.294) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.682 (+/-0.295) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.682 (+/-0.295) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.682 (+/-0.295) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.666 (+/-0.315) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.666 (+/-0.315) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.666 (+/-0.315) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.665 (+/-0.315) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.665 (+/-0.315) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.665 (+/-0.314) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.682 (+/-0.294) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.682 (+/-0.295) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.682 (+/-0.295) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.682 (+/-0.295) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.666 (+/-0.315) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.666 (+/-0.315) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.666 (+/-0.315) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.665 (+/-0.315) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.665 (+/-0.315) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.665 (+/-0.315) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.682 (+/-0.294) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.682 (+/-0.295) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.682 (+/-0.295) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.682 (+/-0.295) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.666 (+/-0.315) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.666 (+/-0.315) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.666 (+/-0.315) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.665 (+/-0.315) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.665 (+/-0.315) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.665 (+/-0.315) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.665 (+/-0.315) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.682 (+/-0.295) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.682 (+/-0.295) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.682 (+/-0.295) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.666 (+/-0.315) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.666 (+/-0.315) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.666 (+/-0.315) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.665 (+/-0.315) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.665 (+/-0.315) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.665 (+/-0.315) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.665 (+/-0.315) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.665 (+/-0.315) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.682 (+/-0.295) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.682 (+/-0.295) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.666 (+/-0.315) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.666 (+/-0.315) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.666 (+/-0.315) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.665 (+/-0.315) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.665 (+/-0.315) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.665 (+/-0.315) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.665 (+/-0.315) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.665 (+/-0.315) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.665 (+/-0.315) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.663 (+/-0.315) for {'C': 10.0, 'kernel': 'linear'}
0.661 (+/-0.315) for {'C': 100.0, 'kernel': 'linear'}
0.662 (+/-0.227) for {'C': 1000.0, 'kernel': 'linear'}
0.611 (+/-0.241) for {'C': 10000.0, 'kernel': 'linear'}
0.611 (+/-0.240) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.17 0.17 0.17 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6822115384615385
第0轮loop中,tunemodel函数得到的最优参数是: ([{'C': array([52.48074602, 53.45643594, 54.45026528, 55.4625713 , 56.49369748,
57.54399373, 58.61381645, 59.70352866, 60.81350013, 61.94410751,
63.09573445]), 'kernel': ['rbf'], 'gamma': array([0.01584893, 0.01614359, 0.01644372, 0.01674943, 0.01706082,
0.01737801, 0.01770109, 0.01803018, 0.01836538, 0.01870682,
0.01905461])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}], {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.017378008287493765}, 0.6822115384615385)
这是第0次迭代微调C和gamma。
第0次迭代,得到delta: [5.55174071e+00 1.52907636e-03]
执行tunemodel()函数前,使用的grid search parameter是:
[{'C': array([52.48074602, 53.45643594, 54.45026528, 55.4625713 , 56.49369748,
57.54399373, 58.61381645, 59.70352866, 60.81350013, 61.94410751,
63.09573445]), 'kernel': ['rbf'], 'gamma': array([0.01584893, 0.01614359, 0.01644372, 0.01674943, 0.01706082,
0.01737801, 0.01770109, 0.01803018, 0.01836538, 0.01870682,
0.01905461])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.747 (+/-0.449) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.747 (+/-0.449) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.016143585568264868}
0.747 (+/-0.449) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.747 (+/-0.449) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.747 (+/-0.449) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.747 (+/-0.449) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.747 (+/-0.449) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.017701089583174213}
0.747 (+/-0.449) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.018030177408595697}
0.747 (+/-0.449) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.018365383433483477}
0.747 (+/-0.449) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.018706821403658012}
0.747 (+/-0.449) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.747 (+/-0.449) for {'C': 53.45643593969718, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.747 (+/-0.449) for {'C': 53.45643593969718, 'kernel': 'rbf', 'gamma': 0.016143585568264868}
0.747 (+/-0.449) for {'C': 53.45643593969718, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.747 (+/-0.449) for {'C': 53.45643593969718, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.747 (+/-0.449) for {'C': 53.45643593969718, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.747 (+/-0.449) for {'C': 53.45643593969718, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.747 (+/-0.449) for {'C': 53.45643593969718, 'kernel': 'rbf', 'gamma': 0.017701089583174213}
0.747 (+/-0.449) for {'C': 53.45643593969718, 'kernel': 'rbf', 'gamma': 0.018030177408595697}
0.747 (+/-0.449) for {'C': 53.45643593969718, 'kernel': 'rbf', 'gamma': 0.018365383433483477}
0.747 (+/-0.449) for {'C': 53.45643593969718, 'kernel': 'rbf', 'gamma': 0.018706821403658012}
0.747 (+/-0.449) for {'C': 53.45643593969718, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.747 (+/-0.449) for {'C': 54.450265284242114, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.747 (+/-0.449) for {'C': 54.450265284242114, 'kernel': 'rbf', 'gamma': 0.016143585568264868}
0.747 (+/-0.449) for {'C': 54.450265284242114, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.747 (+/-0.449) for {'C': 54.450265284242114, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.747 (+/-0.449) for {'C': 54.450265284242114, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.747 (+/-0.449) for {'C': 54.450265284242114, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.747 (+/-0.449) for {'C': 54.450265284242114, 'kernel': 'rbf', 'gamma': 0.017701089583174213}
0.747 (+/-0.449) for {'C': 54.450265284242114, 'kernel': 'rbf', 'gamma': 0.018030177408595697}
0.747 (+/-0.449) for {'C': 54.450265284242114, 'kernel': 'rbf', 'gamma': 0.018365383433483477}
0.747 (+/-0.449) for {'C': 54.450265284242114, 'kernel': 'rbf', 'gamma': 0.018706821403658012}
0.747 (+/-0.449) for {'C': 54.450265284242114, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.747 (+/-0.449) for {'C': 55.462571295791086, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.747 (+/-0.449) for {'C': 55.462571295791086, 'kernel': 'rbf', 'gamma': 0.016143585568264868}
0.747 (+/-0.449) for {'C': 55.462571295791086, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.747 (+/-0.449) for {'C': 55.462571295791086, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.747 (+/-0.449) for {'C': 55.462571295791086, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.747 (+/-0.449) for {'C': 55.462571295791086, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.747 (+/-0.449) for {'C': 55.462571295791086, 'kernel': 'rbf', 'gamma': 0.017701089583174213}
0.747 (+/-0.449) for {'C': 55.462571295791086, 'kernel': 'rbf', 'gamma': 0.018030177408595697}
0.747 (+/-0.449) for {'C': 55.462571295791086, 'kernel': 'rbf', 'gamma': 0.018365383433483477}
0.747 (+/-0.449) for {'C': 55.462571295791086, 'kernel': 'rbf', 'gamma': 0.018706821403658012}
0.747 (+/-0.449) for {'C': 55.462571295791086, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.747 (+/-0.449) for {'C': 56.49369748123024, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.747 (+/-0.449) for {'C': 56.49369748123024, 'kernel': 'rbf', 'gamma': 0.016143585568264868}
0.747 (+/-0.449) for {'C': 56.49369748123024, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.747 (+/-0.449) for {'C': 56.49369748123024, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.747 (+/-0.449) for {'C': 56.49369748123024, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.747 (+/-0.449) for {'C': 56.49369748123024, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.747 (+/-0.449) for {'C': 56.49369748123024, 'kernel': 'rbf', 'gamma': 0.017701089583174213}
0.747 (+/-0.449) for {'C': 56.49369748123024, 'kernel': 'rbf', 'gamma': 0.018030177408595697}
0.747 (+/-0.449) for {'C': 56.49369748123024, 'kernel': 'rbf', 'gamma': 0.018365383433483477}
0.747 (+/-0.449) for {'C': 56.49369748123024, 'kernel': 'rbf', 'gamma': 0.018706821403658012}
0.747 (+/-0.449) for {'C': 56.49369748123024, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.747 (+/-0.449) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.747 (+/-0.449) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.016143585568264868}
0.747 (+/-0.449) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.747 (+/-0.449) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.747 (+/-0.449) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.747 (+/-0.449) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.747 (+/-0.449) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.017701089583174213}
0.747 (+/-0.449) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.018030177408595697}
0.747 (+/-0.449) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.018365383433483477}
0.747 (+/-0.449) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.018706821403658012}
0.747 (+/-0.449) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.747 (+/-0.449) for {'C': 58.61381645140289, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.747 (+/-0.449) for {'C': 58.61381645140289, 'kernel': 'rbf', 'gamma': 0.016143585568264868}
0.747 (+/-0.449) for {'C': 58.61381645140289, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.747 (+/-0.449) for {'C': 58.61381645140289, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.747 (+/-0.449) for {'C': 58.61381645140289, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.747 (+/-0.449) for {'C': 58.61381645140289, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.747 (+/-0.449) for {'C': 58.61381645140289, 'kernel': 'rbf', 'gamma': 0.017701089583174213}
0.747 (+/-0.449) for {'C': 58.61381645140289, 'kernel': 'rbf', 'gamma': 0.018030177408595697}
0.747 (+/-0.449) for {'C': 58.61381645140289, 'kernel': 'rbf', 'gamma': 0.018365383433483477}
0.747 (+/-0.449) for {'C': 58.61381645140289, 'kernel': 'rbf', 'gamma': 0.018706821403658012}
0.747 (+/-0.449) for {'C': 58.61381645140289, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.747 (+/-0.449) for {'C': 59.70352865838368, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.747 (+/-0.449) for {'C': 59.70352865838368, 'kernel': 'rbf', 'gamma': 0.016143585568264868}
0.747 (+/-0.449) for {'C': 59.70352865838368, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.747 (+/-0.449) for {'C': 59.70352865838368, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.747 (+/-0.449) for {'C': 59.70352865838368, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.747 (+/-0.449) for {'C': 59.70352865838368, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.747 (+/-0.449) for {'C': 59.70352865838368, 'kernel': 'rbf', 'gamma': 0.017701089583174213}
0.747 (+/-0.449) for {'C': 59.70352865838368, 'kernel': 'rbf', 'gamma': 0.018030177408595697}
0.747 (+/-0.449) for {'C': 59.70352865838368, 'kernel': 'rbf', 'gamma': 0.018365383433483477}
0.747 (+/-0.449) for {'C': 59.70352865838368, 'kernel': 'rbf', 'gamma': 0.018706821403658012}
0.748 (+/-0.449) for {'C': 59.70352865838368, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.747 (+/-0.449) for {'C': 60.8135001278718, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.747 (+/-0.449) for {'C': 60.8135001278718, 'kernel': 'rbf', 'gamma': 0.016143585568264868}
0.747 (+/-0.449) for {'C': 60.8135001278718, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.747 (+/-0.449) for {'C': 60.8135001278718, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.747 (+/-0.449) for {'C': 60.8135001278718, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.747 (+/-0.449) for {'C': 60.8135001278718, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.747 (+/-0.449) for {'C': 60.8135001278718, 'kernel': 'rbf', 'gamma': 0.017701089583174213}
0.747 (+/-0.449) for {'C': 60.8135001278718, 'kernel': 'rbf', 'gamma': 0.018030177408595697}
0.747 (+/-0.449) for {'C': 60.8135001278718, 'kernel': 'rbf', 'gamma': 0.018365383433483477}
0.748 (+/-0.449) for {'C': 60.8135001278718, 'kernel': 'rbf', 'gamma': 0.018706821403658012}
0.748 (+/-0.449) for {'C': 60.8135001278718, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.747 (+/-0.449) for {'C': 61.944107507678126, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.747 (+/-0.449) for {'C': 61.944107507678126, 'kernel': 'rbf', 'gamma': 0.016143585568264868}
0.747 (+/-0.449) for {'C': 61.944107507678126, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.747 (+/-0.449) for {'C': 61.944107507678126, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.747 (+/-0.449) for {'C': 61.944107507678126, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.747 (+/-0.449) for {'C': 61.944107507678126, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.747 (+/-0.449) for {'C': 61.944107507678126, 'kernel': 'rbf', 'gamma': 0.017701089583174213}
0.747 (+/-0.449) for {'C': 61.944107507678126, 'kernel': 'rbf', 'gamma': 0.018030177408595697}
0.748 (+/-0.449) for {'C': 61.944107507678126, 'kernel': 'rbf', 'gamma': 0.018365383433483477}
0.748 (+/-0.449) for {'C': 61.944107507678126, 'kernel': 'rbf', 'gamma': 0.018706821403658012}
0.748 (+/-0.449) for {'C': 61.944107507678126, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.747 (+/-0.449) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.747 (+/-0.449) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.016143585568264868}
0.747 (+/-0.449) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.747 (+/-0.449) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.747 (+/-0.449) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.747 (+/-0.449) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.747 (+/-0.449) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.017701089583174213}
0.748 (+/-0.449) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.018030177408595697}
0.748 (+/-0.449) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.018365383433483477}
0.748 (+/-0.449) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.018706821403658012}
0.748 (+/-0.449) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'linear'}
0.706 (+/-0.383) for {'C': 10.0, 'kernel': 'linear'}
0.643 (+/-0.320) for {'C': 100.0, 'kernel': 'linear'}
0.653 (+/-0.293) for {'C': 1000.0, 'kernel': 'linear'}
0.619 (+/-0.322) for {'C': 10000.0, 'kernel': 'linear'}
0.619 (+/-0.322) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.20 0.17 0.18 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.641 (+/-0.236) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.641 (+/-0.236) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.016143585568264868}
0.641 (+/-0.236) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.641 (+/-0.236) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.641 (+/-0.236) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.641 (+/-0.236) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.641 (+/-0.236) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.017701089583174213}
0.641 (+/-0.236) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.018030177408595697}
0.641 (+/-0.236) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.018365383433483477}
0.641 (+/-0.236) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.018706821403658012}
0.641 (+/-0.236) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.641 (+/-0.236) for {'C': 53.45643593969718, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.641 (+/-0.236) for {'C': 53.45643593969718, 'kernel': 'rbf', 'gamma': 0.016143585568264868}
0.641 (+/-0.236) for {'C': 53.45643593969718, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.641 (+/-0.236) for {'C': 53.45643593969718, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.641 (+/-0.236) for {'C': 53.45643593969718, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.641 (+/-0.236) for {'C': 53.45643593969718, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.641 (+/-0.236) for {'C': 53.45643593969718, 'kernel': 'rbf', 'gamma': 0.017701089583174213}
0.641 (+/-0.236) for {'C': 53.45643593969718, 'kernel': 'rbf', 'gamma': 0.018030177408595697}
0.641 (+/-0.236) for {'C': 53.45643593969718, 'kernel': 'rbf', 'gamma': 0.018365383433483477}
0.641 (+/-0.236) for {'C': 53.45643593969718, 'kernel': 'rbf', 'gamma': 0.018706821403658012}
0.641 (+/-0.236) for {'C': 53.45643593969718, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.641 (+/-0.236) for {'C': 54.450265284242114, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.641 (+/-0.236) for {'C': 54.450265284242114, 'kernel': 'rbf', 'gamma': 0.016143585568264868}
0.641 (+/-0.236) for {'C': 54.450265284242114, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.641 (+/-0.236) for {'C': 54.450265284242114, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.641 (+/-0.236) for {'C': 54.450265284242114, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.641 (+/-0.236) for {'C': 54.450265284242114, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.641 (+/-0.236) for {'C': 54.450265284242114, 'kernel': 'rbf', 'gamma': 0.017701089583174213}
0.641 (+/-0.236) for {'C': 54.450265284242114, 'kernel': 'rbf', 'gamma': 0.018030177408595697}
0.641 (+/-0.236) for {'C': 54.450265284242114, 'kernel': 'rbf', 'gamma': 0.018365383433483477}
0.641 (+/-0.236) for {'C': 54.450265284242114, 'kernel': 'rbf', 'gamma': 0.018706821403658012}
0.641 (+/-0.236) for {'C': 54.450265284242114, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.641 (+/-0.236) for {'C': 55.462571295791086, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.641 (+/-0.236) for {'C': 55.462571295791086, 'kernel': 'rbf', 'gamma': 0.016143585568264868}
0.641 (+/-0.236) for {'C': 55.462571295791086, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.641 (+/-0.236) for {'C': 55.462571295791086, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.641 (+/-0.236) for {'C': 55.462571295791086, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.641 (+/-0.236) for {'C': 55.462571295791086, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.641 (+/-0.236) for {'C': 55.462571295791086, 'kernel': 'rbf', 'gamma': 0.017701089583174213}
0.641 (+/-0.236) for {'C': 55.462571295791086, 'kernel': 'rbf', 'gamma': 0.018030177408595697}
0.641 (+/-0.236) for {'C': 55.462571295791086, 'kernel': 'rbf', 'gamma': 0.018365383433483477}
0.641 (+/-0.236) for {'C': 55.462571295791086, 'kernel': 'rbf', 'gamma': 0.018706821403658012}
0.641 (+/-0.236) for {'C': 55.462571295791086, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.641 (+/-0.236) for {'C': 56.49369748123024, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.641 (+/-0.236) for {'C': 56.49369748123024, 'kernel': 'rbf', 'gamma': 0.016143585568264868}
0.641 (+/-0.236) for {'C': 56.49369748123024, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.641 (+/-0.236) for {'C': 56.49369748123024, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.641 (+/-0.236) for {'C': 56.49369748123024, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.641 (+/-0.236) for {'C': 56.49369748123024, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.641 (+/-0.236) for {'C': 56.49369748123024, 'kernel': 'rbf', 'gamma': 0.017701089583174213}
0.641 (+/-0.236) for {'C': 56.49369748123024, 'kernel': 'rbf', 'gamma': 0.018030177408595697}
0.641 (+/-0.236) for {'C': 56.49369748123024, 'kernel': 'rbf', 'gamma': 0.018365383433483477}
0.641 (+/-0.236) for {'C': 56.49369748123024, 'kernel': 'rbf', 'gamma': 0.018706821403658012}
0.641 (+/-0.236) for {'C': 56.49369748123024, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.641 (+/-0.236) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.641 (+/-0.236) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.016143585568264868}
0.641 (+/-0.236) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.641 (+/-0.236) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.641 (+/-0.236) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.641 (+/-0.236) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.641 (+/-0.236) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.017701089583174213}
0.641 (+/-0.236) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.018030177408595697}
0.641 (+/-0.236) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.018365383433483477}
0.641 (+/-0.236) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.018706821403658012}
0.641 (+/-0.236) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.641 (+/-0.236) for {'C': 58.61381645140289, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.641 (+/-0.236) for {'C': 58.61381645140289, 'kernel': 'rbf', 'gamma': 0.016143585568264868}
0.641 (+/-0.236) for {'C': 58.61381645140289, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.641 (+/-0.236) for {'C': 58.61381645140289, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.641 (+/-0.236) for {'C': 58.61381645140289, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.641 (+/-0.236) for {'C': 58.61381645140289, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.641 (+/-0.236) for {'C': 58.61381645140289, 'kernel': 'rbf', 'gamma': 0.017701089583174213}
0.641 (+/-0.236) for {'C': 58.61381645140289, 'kernel': 'rbf', 'gamma': 0.018030177408595697}
0.641 (+/-0.236) for {'C': 58.61381645140289, 'kernel': 'rbf', 'gamma': 0.018365383433483477}
0.641 (+/-0.236) for {'C': 58.61381645140289, 'kernel': 'rbf', 'gamma': 0.018706821403658012}
0.641 (+/-0.236) for {'C': 58.61381645140289, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.641 (+/-0.236) for {'C': 59.70352865838368, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.641 (+/-0.236) for {'C': 59.70352865838368, 'kernel': 'rbf', 'gamma': 0.016143585568264868}
0.641 (+/-0.236) for {'C': 59.70352865838368, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.641 (+/-0.236) for {'C': 59.70352865838368, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.641 (+/-0.236) for {'C': 59.70352865838368, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.641 (+/-0.236) for {'C': 59.70352865838368, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.641 (+/-0.236) for {'C': 59.70352865838368, 'kernel': 'rbf', 'gamma': 0.017701089583174213}
0.641 (+/-0.236) for {'C': 59.70352865838368, 'kernel': 'rbf', 'gamma': 0.018030177408595697}
0.641 (+/-0.236) for {'C': 59.70352865838368, 'kernel': 'rbf', 'gamma': 0.018365383433483477}
0.641 (+/-0.236) for {'C': 59.70352865838368, 'kernel': 'rbf', 'gamma': 0.018706821403658012}
0.666 (+/-0.316) for {'C': 59.70352865838368, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.641 (+/-0.236) for {'C': 60.8135001278718, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.641 (+/-0.236) for {'C': 60.8135001278718, 'kernel': 'rbf', 'gamma': 0.016143585568264868}
0.641 (+/-0.236) for {'C': 60.8135001278718, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.641 (+/-0.236) for {'C': 60.8135001278718, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.641 (+/-0.236) for {'C': 60.8135001278718, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.641 (+/-0.236) for {'C': 60.8135001278718, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.641 (+/-0.236) for {'C': 60.8135001278718, 'kernel': 'rbf', 'gamma': 0.017701089583174213}
0.641 (+/-0.236) for {'C': 60.8135001278718, 'kernel': 'rbf', 'gamma': 0.018030177408595697}
0.641 (+/-0.236) for {'C': 60.8135001278718, 'kernel': 'rbf', 'gamma': 0.018365383433483477}
0.666 (+/-0.316) for {'C': 60.8135001278718, 'kernel': 'rbf', 'gamma': 0.018706821403658012}
0.666 (+/-0.316) for {'C': 60.8135001278718, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.641 (+/-0.236) for {'C': 61.944107507678126, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.641 (+/-0.236) for {'C': 61.944107507678126, 'kernel': 'rbf', 'gamma': 0.016143585568264868}
0.641 (+/-0.236) for {'C': 61.944107507678126, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.641 (+/-0.236) for {'C': 61.944107507678126, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.641 (+/-0.236) for {'C': 61.944107507678126, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.641 (+/-0.236) for {'C': 61.944107507678126, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.641 (+/-0.236) for {'C': 61.944107507678126, 'kernel': 'rbf', 'gamma': 0.017701089583174213}
0.641 (+/-0.236) for {'C': 61.944107507678126, 'kernel': 'rbf', 'gamma': 0.018030177408595697}
0.666 (+/-0.316) for {'C': 61.944107507678126, 'kernel': 'rbf', 'gamma': 0.018365383433483477}
0.666 (+/-0.316) for {'C': 61.944107507678126, 'kernel': 'rbf', 'gamma': 0.018706821403658012}
0.666 (+/-0.316) for {'C': 61.944107507678126, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.641 (+/-0.236) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.641 (+/-0.236) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.016143585568264868}
0.641 (+/-0.236) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.641 (+/-0.236) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.641 (+/-0.236) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.641 (+/-0.236) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.641 (+/-0.236) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.017701089583174213}
0.666 (+/-0.316) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.018030177408595697}
0.666 (+/-0.316) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.018365383433483477}
0.666 (+/-0.316) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.018706821403658012}
0.666 (+/-0.316) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'linear'}
0.666 (+/-0.315) for {'C': 10.0, 'kernel': 'linear'}
0.637 (+/-0.237) for {'C': 100.0, 'kernel': 'linear'}
0.662 (+/-0.227) for {'C': 1000.0, 'kernel': 'linear'}
0.611 (+/-0.241) for {'C': 10000.0, 'kernel': 'linear'}
0.611 (+/-0.240) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.20 0.17 0.18 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.99 0.99 629
本轮grid search结果,得到最好的参数选择是: {'C': 59.70352865838368, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6663461538461538
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.727 (+/-0.397) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.727 (+/-0.397) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.016143585568264868}
0.727 (+/-0.397) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.735 (+/-0.402) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.735 (+/-0.402) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.710 (+/-0.363) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.710 (+/-0.363) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.017701089583174213}
0.710 (+/-0.363) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.018030177408595697}
0.710 (+/-0.363) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.018365383433483477}
0.714 (+/-0.359) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.018706821403658012}
0.664 (+/-0.326) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.727 (+/-0.397) for {'C': 53.45643593969718, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.727 (+/-0.397) for {'C': 53.45643593969718, 'kernel': 'rbf', 'gamma': 0.016143585568264868}
0.735 (+/-0.402) for {'C': 53.45643593969718, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.735 (+/-0.402) for {'C': 53.45643593969718, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.710 (+/-0.363) for {'C': 53.45643593969718, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.710 (+/-0.363) for {'C': 53.45643593969718, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.710 (+/-0.363) for {'C': 53.45643593969718, 'kernel': 'rbf', 'gamma': 0.017701089583174213}
0.710 (+/-0.363) for {'C': 53.45643593969718, 'kernel': 'rbf', 'gamma': 0.018030177408595697}
0.714 (+/-0.359) for {'C': 53.45643593969718, 'kernel': 'rbf', 'gamma': 0.018365383433483477}
0.714 (+/-0.359) for {'C': 53.45643593969718, 'kernel': 'rbf', 'gamma': 0.018706821403658012}
0.714 (+/-0.359) for {'C': 53.45643593969718, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.727 (+/-0.397) for {'C': 54.450265284242114, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.735 (+/-0.402) for {'C': 54.450265284242114, 'kernel': 'rbf', 'gamma': 0.016143585568264868}
0.735 (+/-0.402) for {'C': 54.450265284242114, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.710 (+/-0.363) for {'C': 54.450265284242114, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.710 (+/-0.363) for {'C': 54.450265284242114, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.710 (+/-0.363) for {'C': 54.450265284242114, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.710 (+/-0.363) for {'C': 54.450265284242114, 'kernel': 'rbf', 'gamma': 0.017701089583174213}
0.714 (+/-0.359) for {'C': 54.450265284242114, 'kernel': 'rbf', 'gamma': 0.018030177408595697}
0.714 (+/-0.359) for {'C': 54.450265284242114, 'kernel': 'rbf', 'gamma': 0.018365383433483477}
0.714 (+/-0.359) for {'C': 54.450265284242114, 'kernel': 'rbf', 'gamma': 0.018706821403658012}
0.714 (+/-0.359) for {'C': 54.450265284242114, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.735 (+/-0.402) for {'C': 55.462571295791086, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.735 (+/-0.402) for {'C': 55.462571295791086, 'kernel': 'rbf', 'gamma': 0.016143585568264868}
0.710 (+/-0.363) for {'C': 55.462571295791086, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.710 (+/-0.363) for {'C': 55.462571295791086, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.710 (+/-0.363) for {'C': 55.462571295791086, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.710 (+/-0.363) for {'C': 55.462571295791086, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.714 (+/-0.359) for {'C': 55.462571295791086, 'kernel': 'rbf', 'gamma': 0.017701089583174213}
0.714 (+/-0.359) for {'C': 55.462571295791086, 'kernel': 'rbf', 'gamma': 0.018030177408595697}
0.714 (+/-0.359) for {'C': 55.462571295791086, 'kernel': 'rbf', 'gamma': 0.018365383433483477}
0.714 (+/-0.359) for {'C': 55.462571295791086, 'kernel': 'rbf', 'gamma': 0.018706821403658012}
0.664 (+/-0.326) for {'C': 55.462571295791086, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.735 (+/-0.402) for {'C': 56.49369748123024, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.710 (+/-0.363) for {'C': 56.49369748123024, 'kernel': 'rbf', 'gamma': 0.016143585568264868}
0.710 (+/-0.363) for {'C': 56.49369748123024, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.710 (+/-0.363) for {'C': 56.49369748123024, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.714 (+/-0.359) for {'C': 56.49369748123024, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.714 (+/-0.359) for {'C': 56.49369748123024, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.714 (+/-0.359) for {'C': 56.49369748123024, 'kernel': 'rbf', 'gamma': 0.017701089583174213}
0.714 (+/-0.359) for {'C': 56.49369748123024, 'kernel': 'rbf', 'gamma': 0.018030177408595697}
0.714 (+/-0.359) for {'C': 56.49369748123024, 'kernel': 'rbf', 'gamma': 0.018365383433483477}
0.664 (+/-0.326) for {'C': 56.49369748123024, 'kernel': 'rbf', 'gamma': 0.018706821403658012}
0.664 (+/-0.326) for {'C': 56.49369748123024, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.710 (+/-0.363) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.710 (+/-0.363) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.016143585568264868}
0.710 (+/-0.363) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.714 (+/-0.359) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.714 (+/-0.359) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.714 (+/-0.359) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.714 (+/-0.359) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.017701089583174213}
0.714 (+/-0.359) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.018030177408595697}
0.714 (+/-0.359) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.018365383433483477}
0.714 (+/-0.359) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.018706821403658012}
0.664 (+/-0.326) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.710 (+/-0.363) for {'C': 58.61381645140289, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.710 (+/-0.363) for {'C': 58.61381645140289, 'kernel': 'rbf', 'gamma': 0.016143585568264868}
0.714 (+/-0.359) for {'C': 58.61381645140289, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.714 (+/-0.359) for {'C': 58.61381645140289, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.714 (+/-0.359) for {'C': 58.61381645140289, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.714 (+/-0.359) for {'C': 58.61381645140289, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.714 (+/-0.359) for {'C': 58.61381645140289, 'kernel': 'rbf', 'gamma': 0.017701089583174213}
0.714 (+/-0.359) for {'C': 58.61381645140289, 'kernel': 'rbf', 'gamma': 0.018030177408595697}
0.714 (+/-0.359) for {'C': 58.61381645140289, 'kernel': 'rbf', 'gamma': 0.018365383433483477}
0.664 (+/-0.326) for {'C': 58.61381645140289, 'kernel': 'rbf', 'gamma': 0.018706821403658012}
0.664 (+/-0.326) for {'C': 58.61381645140289, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.710 (+/-0.363) for {'C': 59.70352865838368, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.714 (+/-0.359) for {'C': 59.70352865838368, 'kernel': 'rbf', 'gamma': 0.016143585568264868}
0.714 (+/-0.359) for {'C': 59.70352865838368, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.714 (+/-0.359) for {'C': 59.70352865838368, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.714 (+/-0.359) for {'C': 59.70352865838368, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.714 (+/-0.359) for {'C': 59.70352865838368, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.714 (+/-0.359) for {'C': 59.70352865838368, 'kernel': 'rbf', 'gamma': 0.017701089583174213}
0.714 (+/-0.359) for {'C': 59.70352865838368, 'kernel': 'rbf', 'gamma': 0.018030177408595697}
0.664 (+/-0.326) for {'C': 59.70352865838368, 'kernel': 'rbf', 'gamma': 0.018365383433483477}
0.664 (+/-0.326) for {'C': 59.70352865838368, 'kernel': 'rbf', 'gamma': 0.018706821403658012}
0.673 (+/-0.330) for {'C': 59.70352865838368, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.714 (+/-0.359) for {'C': 60.8135001278718, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.714 (+/-0.359) for {'C': 60.8135001278718, 'kernel': 'rbf', 'gamma': 0.016143585568264868}
0.714 (+/-0.359) for {'C': 60.8135001278718, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.714 (+/-0.359) for {'C': 60.8135001278718, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.714 (+/-0.359) for {'C': 60.8135001278718, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.714 (+/-0.359) for {'C': 60.8135001278718, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.714 (+/-0.359) for {'C': 60.8135001278718, 'kernel': 'rbf', 'gamma': 0.017701089583174213}
0.664 (+/-0.326) for {'C': 60.8135001278718, 'kernel': 'rbf', 'gamma': 0.018030177408595697}
0.664 (+/-0.326) for {'C': 60.8135001278718, 'kernel': 'rbf', 'gamma': 0.018365383433483477}
0.673 (+/-0.330) for {'C': 60.8135001278718, 'kernel': 'rbf', 'gamma': 0.018706821403658012}
0.673 (+/-0.330) for {'C': 60.8135001278718, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.714 (+/-0.359) for {'C': 61.944107507678126, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.714 (+/-0.359) for {'C': 61.944107507678126, 'kernel': 'rbf', 'gamma': 0.016143585568264868}
0.714 (+/-0.359) for {'C': 61.944107507678126, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.714 (+/-0.359) for {'C': 61.944107507678126, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.714 (+/-0.359) for {'C': 61.944107507678126, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.714 (+/-0.359) for {'C': 61.944107507678126, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.664 (+/-0.326) for {'C': 61.944107507678126, 'kernel': 'rbf', 'gamma': 0.017701089583174213}
0.664 (+/-0.326) for {'C': 61.944107507678126, 'kernel': 'rbf', 'gamma': 0.018030177408595697}
0.673 (+/-0.330) for {'C': 61.944107507678126, 'kernel': 'rbf', 'gamma': 0.018365383433483477}
0.673 (+/-0.330) for {'C': 61.944107507678126, 'kernel': 'rbf', 'gamma': 0.018706821403658012}
0.673 (+/-0.330) for {'C': 61.944107507678126, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.714 (+/-0.359) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.714 (+/-0.359) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.016143585568264868}
0.714 (+/-0.359) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.714 (+/-0.359) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.714 (+/-0.359) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.714 (+/-0.359) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.664 (+/-0.326) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.017701089583174213}
0.673 (+/-0.330) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.018030177408595697}
0.673 (+/-0.330) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.018365383433483477}
0.673 (+/-0.330) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.018706821403658012}
0.673 (+/-0.330) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 1.0, 'kernel': 'linear'}
0.673 (+/-0.377) for {'C': 10.0, 'kernel': 'linear'}
0.631 (+/-0.312) for {'C': 100.0, 'kernel': 'linear'}
0.653 (+/-0.293) for {'C': 1000.0, 'kernel': 'linear'}
0.619 (+/-0.322) for {'C': 10000.0, 'kernel': 'linear'}
0.619 (+/-0.322) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.20 0.17 0.18 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.682 (+/-0.294) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.682 (+/-0.294) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.016143585568264868}
0.682 (+/-0.294) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.682 (+/-0.295) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.682 (+/-0.295) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.682 (+/-0.295) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.682 (+/-0.295) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.017701089583174213}
0.682 (+/-0.295) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.018030177408595697}
0.682 (+/-0.295) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.018365383433483477}
0.682 (+/-0.295) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.018706821403658012}
0.666 (+/-0.315) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.682 (+/-0.294) for {'C': 53.45643593969718, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.682 (+/-0.294) for {'C': 53.45643593969718, 'kernel': 'rbf', 'gamma': 0.016143585568264868}
0.682 (+/-0.295) for {'C': 53.45643593969718, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.682 (+/-0.295) for {'C': 53.45643593969718, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.682 (+/-0.295) for {'C': 53.45643593969718, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.682 (+/-0.295) for {'C': 53.45643593969718, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.682 (+/-0.295) for {'C': 53.45643593969718, 'kernel': 'rbf', 'gamma': 0.017701089583174213}
0.682 (+/-0.295) for {'C': 53.45643593969718, 'kernel': 'rbf', 'gamma': 0.018030177408595697}
0.682 (+/-0.295) for {'C': 53.45643593969718, 'kernel': 'rbf', 'gamma': 0.018365383433483477}
0.682 (+/-0.295) for {'C': 53.45643593969718, 'kernel': 'rbf', 'gamma': 0.018706821403658012}
0.682 (+/-0.295) for {'C': 53.45643593969718, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.682 (+/-0.294) for {'C': 54.450265284242114, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.682 (+/-0.295) for {'C': 54.450265284242114, 'kernel': 'rbf', 'gamma': 0.016143585568264868}
0.682 (+/-0.295) for {'C': 54.450265284242114, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.682 (+/-0.295) for {'C': 54.450265284242114, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.682 (+/-0.295) for {'C': 54.450265284242114, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.682 (+/-0.295) for {'C': 54.450265284242114, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.682 (+/-0.295) for {'C': 54.450265284242114, 'kernel': 'rbf', 'gamma': 0.017701089583174213}
0.682 (+/-0.295) for {'C': 54.450265284242114, 'kernel': 'rbf', 'gamma': 0.018030177408595697}
0.682 (+/-0.295) for {'C': 54.450265284242114, 'kernel': 'rbf', 'gamma': 0.018365383433483477}
0.682 (+/-0.295) for {'C': 54.450265284242114, 'kernel': 'rbf', 'gamma': 0.018706821403658012}
0.682 (+/-0.295) for {'C': 54.450265284242114, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.682 (+/-0.295) for {'C': 55.462571295791086, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.682 (+/-0.295) for {'C': 55.462571295791086, 'kernel': 'rbf', 'gamma': 0.016143585568264868}
0.682 (+/-0.295) for {'C': 55.462571295791086, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.682 (+/-0.295) for {'C': 55.462571295791086, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.682 (+/-0.295) for {'C': 55.462571295791086, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.682 (+/-0.295) for {'C': 55.462571295791086, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.682 (+/-0.295) for {'C': 55.462571295791086, 'kernel': 'rbf', 'gamma': 0.017701089583174213}
0.682 (+/-0.295) for {'C': 55.462571295791086, 'kernel': 'rbf', 'gamma': 0.018030177408595697}
0.682 (+/-0.295) for {'C': 55.462571295791086, 'kernel': 'rbf', 'gamma': 0.018365383433483477}
0.682 (+/-0.295) for {'C': 55.462571295791086, 'kernel': 'rbf', 'gamma': 0.018706821403658012}
0.666 (+/-0.315) for {'C': 55.462571295791086, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.682 (+/-0.295) for {'C': 56.49369748123024, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.682 (+/-0.295) for {'C': 56.49369748123024, 'kernel': 'rbf', 'gamma': 0.016143585568264868}
0.682 (+/-0.295) for {'C': 56.49369748123024, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.682 (+/-0.295) for {'C': 56.49369748123024, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.682 (+/-0.295) for {'C': 56.49369748123024, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.682 (+/-0.295) for {'C': 56.49369748123024, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.682 (+/-0.295) for {'C': 56.49369748123024, 'kernel': 'rbf', 'gamma': 0.017701089583174213}
0.682 (+/-0.295) for {'C': 56.49369748123024, 'kernel': 'rbf', 'gamma': 0.018030177408595697}
0.682 (+/-0.295) for {'C': 56.49369748123024, 'kernel': 'rbf', 'gamma': 0.018365383433483477}
0.666 (+/-0.315) for {'C': 56.49369748123024, 'kernel': 'rbf', 'gamma': 0.018706821403658012}
0.666 (+/-0.315) for {'C': 56.49369748123024, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.682 (+/-0.295) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.682 (+/-0.295) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.016143585568264868}
0.682 (+/-0.295) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.682 (+/-0.295) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.682 (+/-0.295) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.682 (+/-0.295) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.682 (+/-0.295) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.017701089583174213}
0.682 (+/-0.295) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.018030177408595697}
0.682 (+/-0.295) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.018365383433483477}
0.682 (+/-0.295) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.018706821403658012}
0.666 (+/-0.315) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.682 (+/-0.295) for {'C': 58.61381645140289, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.682 (+/-0.295) for {'C': 58.61381645140289, 'kernel': 'rbf', 'gamma': 0.016143585568264868}
0.682 (+/-0.295) for {'C': 58.61381645140289, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.682 (+/-0.295) for {'C': 58.61381645140289, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.682 (+/-0.295) for {'C': 58.61381645140289, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.682 (+/-0.295) for {'C': 58.61381645140289, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.682 (+/-0.295) for {'C': 58.61381645140289, 'kernel': 'rbf', 'gamma': 0.017701089583174213}
0.682 (+/-0.295) for {'C': 58.61381645140289, 'kernel': 'rbf', 'gamma': 0.018030177408595697}
0.682 (+/-0.295) for {'C': 58.61381645140289, 'kernel': 'rbf', 'gamma': 0.018365383433483477}
0.666 (+/-0.315) for {'C': 58.61381645140289, 'kernel': 'rbf', 'gamma': 0.018706821403658012}
0.666 (+/-0.315) for {'C': 58.61381645140289, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.682 (+/-0.295) for {'C': 59.70352865838368, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.682 (+/-0.295) for {'C': 59.70352865838368, 'kernel': 'rbf', 'gamma': 0.016143585568264868}
0.682 (+/-0.295) for {'C': 59.70352865838368, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.682 (+/-0.295) for {'C': 59.70352865838368, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.682 (+/-0.295) for {'C': 59.70352865838368, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.682 (+/-0.295) for {'C': 59.70352865838368, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.682 (+/-0.295) for {'C': 59.70352865838368, 'kernel': 'rbf', 'gamma': 0.017701089583174213}
0.682 (+/-0.295) for {'C': 59.70352865838368, 'kernel': 'rbf', 'gamma': 0.018030177408595697}
0.666 (+/-0.315) for {'C': 59.70352865838368, 'kernel': 'rbf', 'gamma': 0.018365383433483477}
0.666 (+/-0.315) for {'C': 59.70352865838368, 'kernel': 'rbf', 'gamma': 0.018706821403658012}
0.666 (+/-0.315) for {'C': 59.70352865838368, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.682 (+/-0.295) for {'C': 60.8135001278718, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.682 (+/-0.295) for {'C': 60.8135001278718, 'kernel': 'rbf', 'gamma': 0.016143585568264868}
0.682 (+/-0.295) for {'C': 60.8135001278718, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.682 (+/-0.295) for {'C': 60.8135001278718, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.682 (+/-0.295) for {'C': 60.8135001278718, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.682 (+/-0.295) for {'C': 60.8135001278718, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.682 (+/-0.295) for {'C': 60.8135001278718, 'kernel': 'rbf', 'gamma': 0.017701089583174213}
0.666 (+/-0.315) for {'C': 60.8135001278718, 'kernel': 'rbf', 'gamma': 0.018030177408595697}
0.666 (+/-0.315) for {'C': 60.8135001278718, 'kernel': 'rbf', 'gamma': 0.018365383433483477}
0.666 (+/-0.315) for {'C': 60.8135001278718, 'kernel': 'rbf', 'gamma': 0.018706821403658012}
0.666 (+/-0.315) for {'C': 60.8135001278718, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.682 (+/-0.295) for {'C': 61.944107507678126, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.682 (+/-0.295) for {'C': 61.944107507678126, 'kernel': 'rbf', 'gamma': 0.016143585568264868}
0.682 (+/-0.295) for {'C': 61.944107507678126, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.682 (+/-0.295) for {'C': 61.944107507678126, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.682 (+/-0.295) for {'C': 61.944107507678126, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.682 (+/-0.295) for {'C': 61.944107507678126, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.666 (+/-0.315) for {'C': 61.944107507678126, 'kernel': 'rbf', 'gamma': 0.017701089583174213}
0.666 (+/-0.315) for {'C': 61.944107507678126, 'kernel': 'rbf', 'gamma': 0.018030177408595697}
0.666 (+/-0.315) for {'C': 61.944107507678126, 'kernel': 'rbf', 'gamma': 0.018365383433483477}
0.666 (+/-0.315) for {'C': 61.944107507678126, 'kernel': 'rbf', 'gamma': 0.018706821403658012}
0.666 (+/-0.315) for {'C': 61.944107507678126, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.682 (+/-0.295) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.682 (+/-0.295) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.016143585568264868}
0.682 (+/-0.295) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.682 (+/-0.295) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.682 (+/-0.295) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.682 (+/-0.295) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.666 (+/-0.315) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.017701089583174213}
0.666 (+/-0.315) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.018030177408595697}
0.666 (+/-0.315) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.018365383433483477}
0.666 (+/-0.315) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.018706821403658012}
0.666 (+/-0.315) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.663 (+/-0.315) for {'C': 10.0, 'kernel': 'linear'}
0.661 (+/-0.315) for {'C': 100.0, 'kernel': 'linear'}
0.662 (+/-0.227) for {'C': 1000.0, 'kernel': 'linear'}
0.611 (+/-0.241) for {'C': 10000.0, 'kernel': 'linear'}
0.611 (+/-0.240) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.17 0.17 0.17 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6822115384615385
发现最优参数C为原先的最大/最小值,直接重新设置超参。
第1轮loop中,tunemodel函数得到的最优参数是: ([{'C': array([5.2480746e-04, 5.2480746e-03, 5.2480746e-02, 5.2480746e-01,
5.2480746e+00, 5.2480746e+01, 5.2480746e+02, 5.2480746e+03,
5.2480746e+04, 5.2480746e+05, 5.2480746e+06]), 'kernel': ['rbf'], 'gamma': array([0.01644372, 0.01650441, 0.01656533, 0.01662647, 0.01668784,
0.01674943, 0.01681125, 0.0168733 , 0.01693558, 0.01699809,
0.01706082])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}], {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.016749428760264376}, 0.6822115384615385)
这是第1次迭代微调C和gamma。
第1次迭代,得到delta: [5.06324771e+00 6.28579527e-04]
执行tunemodel()函数前,使用的grid search parameter是:
[{'C': array([5.2480746e-04, 5.2480746e-03, 5.2480746e-02, 5.2480746e-01,
5.2480746e+00, 5.2480746e+01, 5.2480746e+02, 5.2480746e+03,
5.2480746e+04, 5.2480746e+05, 5.2480746e+06]), 'kernel': ['rbf'], 'gamma': array([0.01644372, 0.01650441, 0.01656533, 0.01662647, 0.01668784,
0.01674943, 0.01681125, 0.0168733 , 0.01693558, 0.01699809,
0.01706082])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.496 (+/-0.001) for {'C': 0.0005248074602497723, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.496 (+/-0.001) for {'C': 0.0005248074602497723, 'kernel': 'rbf', 'gamma': 0.01650440985652279}
0.496 (+/-0.001) for {'C': 0.0005248074602497723, 'kernel': 'rbf', 'gamma': 0.01656532649318018}
0.496 (+/-0.001) for {'C': 0.0005248074602497723, 'kernel': 'rbf', 'gamma': 0.016626467968935365}
0.496 (+/-0.001) for {'C': 0.0005248074602497723, 'kernel': 'rbf', 'gamma': 0.016687835113653904}
0.496 (+/-0.001) for {'C': 0.0005248074602497723, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.496 (+/-0.001) for {'C': 0.0005248074602497723, 'kernel': 'rbf', 'gamma': 0.016811249744769604}
0.496 (+/-0.001) for {'C': 0.0005248074602497723, 'kernel': 'rbf', 'gamma': 0.01687329890625804}
0.496 (+/-0.001) for {'C': 0.0005248074602497723, 'kernel': 'rbf', 'gamma': 0.01693557708691519}
0.496 (+/-0.001) for {'C': 0.0005248074602497723, 'kernel': 'rbf', 'gamma': 0.016998085132034966}
0.496 (+/-0.001) for {'C': 0.0005248074602497723, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.496 (+/-0.001) for {'C': 0.005248074602497723, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.496 (+/-0.001) for {'C': 0.005248074602497723, 'kernel': 'rbf', 'gamma': 0.01650440985652279}
0.496 (+/-0.001) for {'C': 0.005248074602497723, 'kernel': 'rbf', 'gamma': 0.01656532649318018}
0.496 (+/-0.001) for {'C': 0.005248074602497723, 'kernel': 'rbf', 'gamma': 0.016626467968935365}
0.496 (+/-0.001) for {'C': 0.005248074602497723, 'kernel': 'rbf', 'gamma': 0.016687835113653904}
0.496 (+/-0.001) for {'C': 0.005248074602497723, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.496 (+/-0.001) for {'C': 0.005248074602497723, 'kernel': 'rbf', 'gamma': 0.016811249744769604}
0.496 (+/-0.001) for {'C': 0.005248074602497723, 'kernel': 'rbf', 'gamma': 0.01687329890625804}
0.496 (+/-0.001) for {'C': 0.005248074602497723, 'kernel': 'rbf', 'gamma': 0.01693557708691519}
0.496 (+/-0.001) for {'C': 0.005248074602497723, 'kernel': 'rbf', 'gamma': 0.016998085132034966}
0.496 (+/-0.001) for {'C': 0.005248074602497723, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.496 (+/-0.001) for {'C': 0.05248074602497723, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.496 (+/-0.001) for {'C': 0.05248074602497723, 'kernel': 'rbf', 'gamma': 0.01650440985652279}
0.496 (+/-0.001) for {'C': 0.05248074602497723, 'kernel': 'rbf', 'gamma': 0.01656532649318018}
0.496 (+/-0.001) for {'C': 0.05248074602497723, 'kernel': 'rbf', 'gamma': 0.016626467968935365}
0.496 (+/-0.001) for {'C': 0.05248074602497723, 'kernel': 'rbf', 'gamma': 0.016687835113653904}
0.496 (+/-0.001) for {'C': 0.05248074602497723, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.496 (+/-0.001) for {'C': 0.05248074602497723, 'kernel': 'rbf', 'gamma': 0.016811249744769604}
0.496 (+/-0.001) for {'C': 0.05248074602497723, 'kernel': 'rbf', 'gamma': 0.01687329890625804}
0.496 (+/-0.001) for {'C': 0.05248074602497723, 'kernel': 'rbf', 'gamma': 0.01693557708691519}
0.496 (+/-0.001) for {'C': 0.05248074602497723, 'kernel': 'rbf', 'gamma': 0.016998085132034966}
0.496 (+/-0.001) for {'C': 0.05248074602497723, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.496 (+/-0.001) for {'C': 0.5248074602497723, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.496 (+/-0.001) for {'C': 0.5248074602497723, 'kernel': 'rbf', 'gamma': 0.01650440985652279}
0.496 (+/-0.001) for {'C': 0.5248074602497723, 'kernel': 'rbf', 'gamma': 0.01656532649318018}
0.496 (+/-0.001) for {'C': 0.5248074602497723, 'kernel': 'rbf', 'gamma': 0.016626467968935365}
0.496 (+/-0.001) for {'C': 0.5248074602497723, 'kernel': 'rbf', 'gamma': 0.016687835113653904}
0.496 (+/-0.001) for {'C': 0.5248074602497723, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.496 (+/-0.001) for {'C': 0.5248074602497723, 'kernel': 'rbf', 'gamma': 0.016811249744769604}
0.496 (+/-0.001) for {'C': 0.5248074602497723, 'kernel': 'rbf', 'gamma': 0.01687329890625804}
0.496 (+/-0.001) for {'C': 0.5248074602497723, 'kernel': 'rbf', 'gamma': 0.01693557708691519}
0.496 (+/-0.001) for {'C': 0.5248074602497723, 'kernel': 'rbf', 'gamma': 0.016998085132034966}
0.496 (+/-0.001) for {'C': 0.5248074602497723, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.747 (+/-0.502) for {'C': 5.248074602497723, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.747 (+/-0.502) for {'C': 5.248074602497723, 'kernel': 'rbf', 'gamma': 0.01650440985652279}
0.747 (+/-0.502) for {'C': 5.248074602497723, 'kernel': 'rbf', 'gamma': 0.01656532649318018}
0.747 (+/-0.502) for {'C': 5.248074602497723, 'kernel': 'rbf', 'gamma': 0.016626467968935365}
0.747 (+/-0.502) for {'C': 5.248074602497723, 'kernel': 'rbf', 'gamma': 0.016687835113653904}
0.747 (+/-0.502) for {'C': 5.248074602497723, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.747 (+/-0.502) for {'C': 5.248074602497723, 'kernel': 'rbf', 'gamma': 0.016811249744769604}
0.747 (+/-0.502) for {'C': 5.248074602497723, 'kernel': 'rbf', 'gamma': 0.01687329890625804}
0.747 (+/-0.502) for {'C': 5.248074602497723, 'kernel': 'rbf', 'gamma': 0.01693557708691519}
0.747 (+/-0.502) for {'C': 5.248074602497723, 'kernel': 'rbf', 'gamma': 0.016998085132034966}
0.747 (+/-0.502) for {'C': 5.248074602497723, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.747 (+/-0.449) for {'C': 52.48074602497723, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.747 (+/-0.449) for {'C': 52.48074602497723, 'kernel': 'rbf', 'gamma': 0.01650440985652279}
0.747 (+/-0.449) for {'C': 52.48074602497723, 'kernel': 'rbf', 'gamma': 0.01656532649318018}
0.747 (+/-0.449) for {'C': 52.48074602497723, 'kernel': 'rbf', 'gamma': 0.016626467968935365}
0.747 (+/-0.449) for {'C': 52.48074602497723, 'kernel': 'rbf', 'gamma': 0.016687835113653904}
0.747 (+/-0.449) for {'C': 52.48074602497723, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.747 (+/-0.449) for {'C': 52.48074602497723, 'kernel': 'rbf', 'gamma': 0.016811249744769604}
0.747 (+/-0.449) for {'C': 52.48074602497723, 'kernel': 'rbf', 'gamma': 0.01687329890625804}
0.747 (+/-0.449) for {'C': 52.48074602497723, 'kernel': 'rbf', 'gamma': 0.01693557708691519}
0.747 (+/-0.449) for {'C': 52.48074602497723, 'kernel': 'rbf', 'gamma': 0.016998085132034966}
0.747 (+/-0.449) for {'C': 52.48074602497723, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.697 (+/-0.375) for {'C': 524.8074602497722, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.697 (+/-0.375) for {'C': 524.8074602497722, 'kernel': 'rbf', 'gamma': 0.01650440985652279}
0.697 (+/-0.375) for {'C': 524.8074602497722, 'kernel': 'rbf', 'gamma': 0.01656532649318018}
0.697 (+/-0.375) for {'C': 524.8074602497722, 'kernel': 'rbf', 'gamma': 0.016626467968935365}
0.697 (+/-0.375) for {'C': 524.8074602497722, 'kernel': 'rbf', 'gamma': 0.016687835113653904}
0.697 (+/-0.375) for {'C': 524.8074602497722, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.697 (+/-0.375) for {'C': 524.8074602497722, 'kernel': 'rbf', 'gamma': 0.016811249744769604}
0.697 (+/-0.375) for {'C': 524.8074602497722, 'kernel': 'rbf', 'gamma': 0.01687329890625804}
0.697 (+/-0.375) for {'C': 524.8074602497722, 'kernel': 'rbf', 'gamma': 0.01693557708691519}
0.697 (+/-0.375) for {'C': 524.8074602497722, 'kernel': 'rbf', 'gamma': 0.016998085132034966}
0.697 (+/-0.375) for {'C': 524.8074602497722, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.632 (+/-0.311) for {'C': 5248.074602497723, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.629 (+/-0.312) for {'C': 5248.074602497723, 'kernel': 'rbf', 'gamma': 0.01650440985652279}
0.629 (+/-0.312) for {'C': 5248.074602497723, 'kernel': 'rbf', 'gamma': 0.01656532649318018}
0.629 (+/-0.312) for {'C': 5248.074602497723, 'kernel': 'rbf', 'gamma': 0.016626467968935365}
0.629 (+/-0.312) for {'C': 5248.074602497723, 'kernel': 'rbf', 'gamma': 0.016687835113653904}
0.629 (+/-0.312) for {'C': 5248.074602497723, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.629 (+/-0.312) for {'C': 5248.074602497723, 'kernel': 'rbf', 'gamma': 0.016811249744769604}
0.629 (+/-0.312) for {'C': 5248.074602497723, 'kernel': 'rbf', 'gamma': 0.01687329890625804}
0.629 (+/-0.312) for {'C': 5248.074602497723, 'kernel': 'rbf', 'gamma': 0.01693557708691519}
0.629 (+/-0.312) for {'C': 5248.074602497723, 'kernel': 'rbf', 'gamma': 0.016998085132034966}
0.629 (+/-0.312) for {'C': 5248.074602497723, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.614 (+/-0.327) for {'C': 52480.74602497723, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.614 (+/-0.327) for {'C': 52480.74602497723, 'kernel': 'rbf', 'gamma': 0.01650440985652279}
0.614 (+/-0.327) for {'C': 52480.74602497723, 'kernel': 'rbf', 'gamma': 0.01656532649318018}
0.614 (+/-0.327) for {'C': 52480.74602497723, 'kernel': 'rbf', 'gamma': 0.016626467968935365}
0.614 (+/-0.327) for {'C': 52480.74602497723, 'kernel': 'rbf', 'gamma': 0.016687835113653904}
0.614 (+/-0.327) for {'C': 52480.74602497723, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.614 (+/-0.327) for {'C': 52480.74602497723, 'kernel': 'rbf', 'gamma': 0.016811249744769604}
0.614 (+/-0.327) for {'C': 52480.74602497723, 'kernel': 'rbf', 'gamma': 0.01687329890625804}
0.614 (+/-0.327) for {'C': 52480.74602497723, 'kernel': 'rbf', 'gamma': 0.01693557708691519}
0.614 (+/-0.327) for {'C': 52480.74602497723, 'kernel': 'rbf', 'gamma': 0.016998085132034966}
0.614 (+/-0.327) for {'C': 52480.74602497723, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.614 (+/-0.327) for {'C': 524807.4602497723, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.614 (+/-0.327) for {'C': 524807.4602497723, 'kernel': 'rbf', 'gamma': 0.01650440985652279}
0.614 (+/-0.327) for {'C': 524807.4602497723, 'kernel': 'rbf', 'gamma': 0.01656532649318018}
0.614 (+/-0.327) for {'C': 524807.4602497723, 'kernel': 'rbf', 'gamma': 0.016626467968935365}
0.614 (+/-0.327) for {'C': 524807.4602497723, 'kernel': 'rbf', 'gamma': 0.016687835113653904}
0.614 (+/-0.327) for {'C': 524807.4602497723, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.614 (+/-0.327) for {'C': 524807.4602497723, 'kernel': 'rbf', 'gamma': 0.016811249744769604}
0.614 (+/-0.327) for {'C': 524807.4602497723, 'kernel': 'rbf', 'gamma': 0.01687329890625804}
0.614 (+/-0.327) for {'C': 524807.4602497723, 'kernel': 'rbf', 'gamma': 0.01693557708691519}
0.614 (+/-0.327) for {'C': 524807.4602497723, 'kernel': 'rbf', 'gamma': 0.016998085132034966}
0.614 (+/-0.327) for {'C': 524807.4602497723, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.614 (+/-0.327) for {'C': 5248074.602497723, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.614 (+/-0.327) for {'C': 5248074.602497723, 'kernel': 'rbf', 'gamma': 0.01650440985652279}
0.614 (+/-0.327) for {'C': 5248074.602497723, 'kernel': 'rbf', 'gamma': 0.01656532649318018}
0.614 (+/-0.327) for {'C': 5248074.602497723, 'kernel': 'rbf', 'gamma': 0.016626467968935365}
0.614 (+/-0.327) for {'C': 5248074.602497723, 'kernel': 'rbf', 'gamma': 0.016687835113653904}
0.614 (+/-0.327) for {'C': 5248074.602497723, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.614 (+/-0.327) for {'C': 5248074.602497723, 'kernel': 'rbf', 'gamma': 0.016811249744769604}
0.614 (+/-0.327) for {'C': 5248074.602497723, 'kernel': 'rbf', 'gamma': 0.01687329890625804}
0.614 (+/-0.327) for {'C': 5248074.602497723, 'kernel': 'rbf', 'gamma': 0.01693557708691519}
0.614 (+/-0.327) for {'C': 5248074.602497723, 'kernel': 'rbf', 'gamma': 0.016998085132034966}
0.614 (+/-0.327) for {'C': 5248074.602497723, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'linear'}
0.706 (+/-0.383) for {'C': 10.0, 'kernel': 'linear'}
0.643 (+/-0.320) for {'C': 100.0, 'kernel': 'linear'}
0.653 (+/-0.293) for {'C': 1000.0, 'kernel': 'linear'}
0.619 (+/-0.322) for {'C': 10000.0, 'kernel': 'linear'}
0.619 (+/-0.322) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.25 0.17 0.20 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.500 (+/-0.000) for {'C': 0.0005248074602497723, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.500 (+/-0.000) for {'C': 0.0005248074602497723, 'kernel': 'rbf', 'gamma': 0.01650440985652279}
0.500 (+/-0.000) for {'C': 0.0005248074602497723, 'kernel': 'rbf', 'gamma': 0.01656532649318018}
0.500 (+/-0.000) for {'C': 0.0005248074602497723, 'kernel': 'rbf', 'gamma': 0.016626467968935365}
0.500 (+/-0.000) for {'C': 0.0005248074602497723, 'kernel': 'rbf', 'gamma': 0.016687835113653904}
0.500 (+/-0.000) for {'C': 0.0005248074602497723, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.500 (+/-0.000) for {'C': 0.0005248074602497723, 'kernel': 'rbf', 'gamma': 0.016811249744769604}
0.500 (+/-0.000) for {'C': 0.0005248074602497723, 'kernel': 'rbf', 'gamma': 0.01687329890625804}
0.500 (+/-0.000) for {'C': 0.0005248074602497723, 'kernel': 'rbf', 'gamma': 0.01693557708691519}
0.500 (+/-0.000) for {'C': 0.0005248074602497723, 'kernel': 'rbf', 'gamma': 0.016998085132034966}
0.500 (+/-0.000) for {'C': 0.0005248074602497723, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.500 (+/-0.000) for {'C': 0.005248074602497723, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.500 (+/-0.000) for {'C': 0.005248074602497723, 'kernel': 'rbf', 'gamma': 0.01650440985652279}
0.500 (+/-0.000) for {'C': 0.005248074602497723, 'kernel': 'rbf', 'gamma': 0.01656532649318018}
0.500 (+/-0.000) for {'C': 0.005248074602497723, 'kernel': 'rbf', 'gamma': 0.016626467968935365}
0.500 (+/-0.000) for {'C': 0.005248074602497723, 'kernel': 'rbf', 'gamma': 0.016687835113653904}
0.500 (+/-0.000) for {'C': 0.005248074602497723, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.500 (+/-0.000) for {'C': 0.005248074602497723, 'kernel': 'rbf', 'gamma': 0.016811249744769604}
0.500 (+/-0.000) for {'C': 0.005248074602497723, 'kernel': 'rbf', 'gamma': 0.01687329890625804}
0.500 (+/-0.000) for {'C': 0.005248074602497723, 'kernel': 'rbf', 'gamma': 0.01693557708691519}
0.500 (+/-0.000) for {'C': 0.005248074602497723, 'kernel': 'rbf', 'gamma': 0.016998085132034966}
0.500 (+/-0.000) for {'C': 0.005248074602497723, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.500 (+/-0.000) for {'C': 0.05248074602497723, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.500 (+/-0.000) for {'C': 0.05248074602497723, 'kernel': 'rbf', 'gamma': 0.01650440985652279}
0.500 (+/-0.000) for {'C': 0.05248074602497723, 'kernel': 'rbf', 'gamma': 0.01656532649318018}
0.500 (+/-0.000) for {'C': 0.05248074602497723, 'kernel': 'rbf', 'gamma': 0.016626467968935365}
0.500 (+/-0.000) for {'C': 0.05248074602497723, 'kernel': 'rbf', 'gamma': 0.016687835113653904}
0.500 (+/-0.000) for {'C': 0.05248074602497723, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.500 (+/-0.000) for {'C': 0.05248074602497723, 'kernel': 'rbf', 'gamma': 0.016811249744769604}
0.500 (+/-0.000) for {'C': 0.05248074602497723, 'kernel': 'rbf', 'gamma': 0.01687329890625804}
0.500 (+/-0.000) for {'C': 0.05248074602497723, 'kernel': 'rbf', 'gamma': 0.01693557708691519}
0.500 (+/-0.000) for {'C': 0.05248074602497723, 'kernel': 'rbf', 'gamma': 0.016998085132034966}
0.500 (+/-0.000) for {'C': 0.05248074602497723, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.500 (+/-0.000) for {'C': 0.5248074602497723, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.500 (+/-0.000) for {'C': 0.5248074602497723, 'kernel': 'rbf', 'gamma': 0.01650440985652279}
0.500 (+/-0.000) for {'C': 0.5248074602497723, 'kernel': 'rbf', 'gamma': 0.01656532649318018}
0.500 (+/-0.000) for {'C': 0.5248074602497723, 'kernel': 'rbf', 'gamma': 0.016626467968935365}
0.500 (+/-0.000) for {'C': 0.5248074602497723, 'kernel': 'rbf', 'gamma': 0.016687835113653904}
0.500 (+/-0.000) for {'C': 0.5248074602497723, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.500 (+/-0.000) for {'C': 0.5248074602497723, 'kernel': 'rbf', 'gamma': 0.016811249744769604}
0.500 (+/-0.000) for {'C': 0.5248074602497723, 'kernel': 'rbf', 'gamma': 0.01687329890625804}
0.500 (+/-0.000) for {'C': 0.5248074602497723, 'kernel': 'rbf', 'gamma': 0.01693557708691519}
0.500 (+/-0.000) for {'C': 0.5248074602497723, 'kernel': 'rbf', 'gamma': 0.016998085132034966}
0.500 (+/-0.000) for {'C': 0.5248074602497723, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.617 (+/-0.238) for {'C': 5.248074602497723, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.617 (+/-0.238) for {'C': 5.248074602497723, 'kernel': 'rbf', 'gamma': 0.01650440985652279}
0.617 (+/-0.238) for {'C': 5.248074602497723, 'kernel': 'rbf', 'gamma': 0.01656532649318018}
0.617 (+/-0.238) for {'C': 5.248074602497723, 'kernel': 'rbf', 'gamma': 0.016626467968935365}
0.617 (+/-0.238) for {'C': 5.248074602497723, 'kernel': 'rbf', 'gamma': 0.016687835113653904}
0.617 (+/-0.238) for {'C': 5.248074602497723, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.617 (+/-0.238) for {'C': 5.248074602497723, 'kernel': 'rbf', 'gamma': 0.016811249744769604}
0.617 (+/-0.238) for {'C': 5.248074602497723, 'kernel': 'rbf', 'gamma': 0.01687329890625804}
0.617 (+/-0.238) for {'C': 5.248074602497723, 'kernel': 'rbf', 'gamma': 0.01693557708691519}
0.617 (+/-0.238) for {'C': 5.248074602497723, 'kernel': 'rbf', 'gamma': 0.016998085132034966}
0.617 (+/-0.238) for {'C': 5.248074602497723, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.641 (+/-0.236) for {'C': 52.48074602497723, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.641 (+/-0.236) for {'C': 52.48074602497723, 'kernel': 'rbf', 'gamma': 0.01650440985652279}
0.641 (+/-0.236) for {'C': 52.48074602497723, 'kernel': 'rbf', 'gamma': 0.01656532649318018}
0.641 (+/-0.236) for {'C': 52.48074602497723, 'kernel': 'rbf', 'gamma': 0.016626467968935365}
0.641 (+/-0.236) for {'C': 52.48074602497723, 'kernel': 'rbf', 'gamma': 0.016687835113653904}
0.641 (+/-0.236) for {'C': 52.48074602497723, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.641 (+/-0.236) for {'C': 52.48074602497723, 'kernel': 'rbf', 'gamma': 0.016811249744769604}
0.641 (+/-0.236) for {'C': 52.48074602497723, 'kernel': 'rbf', 'gamma': 0.01687329890625804}
0.641 (+/-0.236) for {'C': 52.48074602497723, 'kernel': 'rbf', 'gamma': 0.01693557708691519}
0.641 (+/-0.236) for {'C': 52.48074602497723, 'kernel': 'rbf', 'gamma': 0.016998085132034966}
0.641 (+/-0.236) for {'C': 52.48074602497723, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.641 (+/-0.236) for {'C': 524.8074602497722, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.641 (+/-0.236) for {'C': 524.8074602497722, 'kernel': 'rbf', 'gamma': 0.01650440985652279}
0.641 (+/-0.236) for {'C': 524.8074602497722, 'kernel': 'rbf', 'gamma': 0.01656532649318018}
0.641 (+/-0.236) for {'C': 524.8074602497722, 'kernel': 'rbf', 'gamma': 0.016626467968935365}
0.641 (+/-0.236) for {'C': 524.8074602497722, 'kernel': 'rbf', 'gamma': 0.016687835113653904}
0.641 (+/-0.236) for {'C': 524.8074602497722, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.641 (+/-0.236) for {'C': 524.8074602497722, 'kernel': 'rbf', 'gamma': 0.016811249744769604}
0.641 (+/-0.236) for {'C': 524.8074602497722, 'kernel': 'rbf', 'gamma': 0.01687329890625804}
0.641 (+/-0.236) for {'C': 524.8074602497722, 'kernel': 'rbf', 'gamma': 0.01693557708691519}
0.641 (+/-0.236) for {'C': 524.8074602497722, 'kernel': 'rbf', 'gamma': 0.016998085132034966}
0.641 (+/-0.236) for {'C': 524.8074602497722, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.637 (+/-0.237) for {'C': 5248.074602497723, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.637 (+/-0.237) for {'C': 5248.074602497723, 'kernel': 'rbf', 'gamma': 0.01650440985652279}
0.637 (+/-0.237) for {'C': 5248.074602497723, 'kernel': 'rbf', 'gamma': 0.01656532649318018}
0.637 (+/-0.237) for {'C': 5248.074602497723, 'kernel': 'rbf', 'gamma': 0.016626467968935365}
0.637 (+/-0.237) for {'C': 5248.074602497723, 'kernel': 'rbf', 'gamma': 0.016687835113653904}
0.637 (+/-0.237) for {'C': 5248.074602497723, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.637 (+/-0.237) for {'C': 5248.074602497723, 'kernel': 'rbf', 'gamma': 0.016811249744769604}
0.637 (+/-0.237) for {'C': 5248.074602497723, 'kernel': 'rbf', 'gamma': 0.01687329890625804}
0.637 (+/-0.237) for {'C': 5248.074602497723, 'kernel': 'rbf', 'gamma': 0.01693557708691519}
0.637 (+/-0.237) for {'C': 5248.074602497723, 'kernel': 'rbf', 'gamma': 0.016998085132034966}
0.637 (+/-0.237) for {'C': 5248.074602497723, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.588 (+/-0.232) for {'C': 52480.74602497723, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.588 (+/-0.232) for {'C': 52480.74602497723, 'kernel': 'rbf', 'gamma': 0.01650440985652279}
0.588 (+/-0.232) for {'C': 52480.74602497723, 'kernel': 'rbf', 'gamma': 0.01656532649318018}
0.588 (+/-0.232) for {'C': 52480.74602497723, 'kernel': 'rbf', 'gamma': 0.016626467968935365}
0.588 (+/-0.232) for {'C': 52480.74602497723, 'kernel': 'rbf', 'gamma': 0.016687835113653904}
0.588 (+/-0.232) for {'C': 52480.74602497723, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.588 (+/-0.232) for {'C': 52480.74602497723, 'kernel': 'rbf', 'gamma': 0.016811249744769604}
0.588 (+/-0.232) for {'C': 52480.74602497723, 'kernel': 'rbf', 'gamma': 0.01687329890625804}
0.588 (+/-0.232) for {'C': 52480.74602497723, 'kernel': 'rbf', 'gamma': 0.01693557708691519}
0.588 (+/-0.232) for {'C': 52480.74602497723, 'kernel': 'rbf', 'gamma': 0.016998085132034966}
0.588 (+/-0.232) for {'C': 52480.74602497723, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.587 (+/-0.234) for {'C': 524807.4602497723, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.587 (+/-0.234) for {'C': 524807.4602497723, 'kernel': 'rbf', 'gamma': 0.01650440985652279}
0.587 (+/-0.234) for {'C': 524807.4602497723, 'kernel': 'rbf', 'gamma': 0.01656532649318018}
0.587 (+/-0.234) for {'C': 524807.4602497723, 'kernel': 'rbf', 'gamma': 0.016626467968935365}
0.587 (+/-0.234) for {'C': 524807.4602497723, 'kernel': 'rbf', 'gamma': 0.016687835113653904}
0.587 (+/-0.234) for {'C': 524807.4602497723, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.587 (+/-0.234) for {'C': 524807.4602497723, 'kernel': 'rbf', 'gamma': 0.016811249744769604}
0.587 (+/-0.234) for {'C': 524807.4602497723, 'kernel': 'rbf', 'gamma': 0.01687329890625804}
0.587 (+/-0.234) for {'C': 524807.4602497723, 'kernel': 'rbf', 'gamma': 0.01693557708691519}
0.587 (+/-0.234) for {'C': 524807.4602497723, 'kernel': 'rbf', 'gamma': 0.016998085132034966}
0.587 (+/-0.234) for {'C': 524807.4602497723, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.587 (+/-0.234) for {'C': 5248074.602497723, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.587 (+/-0.234) for {'C': 5248074.602497723, 'kernel': 'rbf', 'gamma': 0.01650440985652279}
0.587 (+/-0.234) for {'C': 5248074.602497723, 'kernel': 'rbf', 'gamma': 0.01656532649318018}
0.587 (+/-0.234) for {'C': 5248074.602497723, 'kernel': 'rbf', 'gamma': 0.016626467968935365}
0.587 (+/-0.234) for {'C': 5248074.602497723, 'kernel': 'rbf', 'gamma': 0.016687835113653904}
0.587 (+/-0.234) for {'C': 5248074.602497723, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.587 (+/-0.234) for {'C': 5248074.602497723, 'kernel': 'rbf', 'gamma': 0.016811249744769604}
0.587 (+/-0.234) for {'C': 5248074.602497723, 'kernel': 'rbf', 'gamma': 0.01687329890625804}
0.587 (+/-0.234) for {'C': 5248074.602497723, 'kernel': 'rbf', 'gamma': 0.01693557708691519}
0.587 (+/-0.234) for {'C': 5248074.602497723, 'kernel': 'rbf', 'gamma': 0.016998085132034966}
0.587 (+/-0.234) for {'C': 5248074.602497723, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'linear'}
0.666 (+/-0.315) for {'C': 10.0, 'kernel': 'linear'}
0.637 (+/-0.237) for {'C': 100.0, 'kernel': 'linear'}
0.662 (+/-0.227) for {'C': 1000.0, 'kernel': 'linear'}
0.611 (+/-0.241) for {'C': 10000.0, 'kernel': 'linear'}
0.611 (+/-0.240) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.17 0.17 0.17 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 10.0, 'kernel': 'linear'}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6658653846153846
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.496 (+/-0.001) for {'C': 0.0005248074602497723, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.496 (+/-0.001) for {'C': 0.0005248074602497723, 'kernel': 'rbf', 'gamma': 0.01650440985652279}
0.496 (+/-0.001) for {'C': 0.0005248074602497723, 'kernel': 'rbf', 'gamma': 0.01656532649318018}
0.496 (+/-0.001) for {'C': 0.0005248074602497723, 'kernel': 'rbf', 'gamma': 0.016626467968935365}
0.496 (+/-0.001) for {'C': 0.0005248074602497723, 'kernel': 'rbf', 'gamma': 0.016687835113653904}
0.496 (+/-0.001) for {'C': 0.0005248074602497723, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.496 (+/-0.001) for {'C': 0.0005248074602497723, 'kernel': 'rbf', 'gamma': 0.016811249744769604}
0.496 (+/-0.001) for {'C': 0.0005248074602497723, 'kernel': 'rbf', 'gamma': 0.01687329890625804}
0.496 (+/-0.001) for {'C': 0.0005248074602497723, 'kernel': 'rbf', 'gamma': 0.01693557708691519}
0.496 (+/-0.001) for {'C': 0.0005248074602497723, 'kernel': 'rbf', 'gamma': 0.016998085132034966}
0.496 (+/-0.001) for {'C': 0.0005248074602497723, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.496 (+/-0.001) for {'C': 0.005248074602497723, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.496 (+/-0.001) for {'C': 0.005248074602497723, 'kernel': 'rbf', 'gamma': 0.01650440985652279}
0.496 (+/-0.001) for {'C': 0.005248074602497723, 'kernel': 'rbf', 'gamma': 0.01656532649318018}
0.496 (+/-0.001) for {'C': 0.005248074602497723, 'kernel': 'rbf', 'gamma': 0.016626467968935365}
0.496 (+/-0.001) for {'C': 0.005248074602497723, 'kernel': 'rbf', 'gamma': 0.016687835113653904}
0.496 (+/-0.001) for {'C': 0.005248074602497723, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.496 (+/-0.001) for {'C': 0.005248074602497723, 'kernel': 'rbf', 'gamma': 0.016811249744769604}
0.496 (+/-0.001) for {'C': 0.005248074602497723, 'kernel': 'rbf', 'gamma': 0.01687329890625804}
0.496 (+/-0.001) for {'C': 0.005248074602497723, 'kernel': 'rbf', 'gamma': 0.01693557708691519}
0.496 (+/-0.001) for {'C': 0.005248074602497723, 'kernel': 'rbf', 'gamma': 0.016998085132034966}
0.496 (+/-0.001) for {'C': 0.005248074602497723, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.496 (+/-0.001) for {'C': 0.05248074602497723, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.496 (+/-0.001) for {'C': 0.05248074602497723, 'kernel': 'rbf', 'gamma': 0.01650440985652279}
0.496 (+/-0.001) for {'C': 0.05248074602497723, 'kernel': 'rbf', 'gamma': 0.01656532649318018}
0.496 (+/-0.001) for {'C': 0.05248074602497723, 'kernel': 'rbf', 'gamma': 0.016626467968935365}
0.496 (+/-0.001) for {'C': 0.05248074602497723, 'kernel': 'rbf', 'gamma': 0.016687835113653904}
0.496 (+/-0.001) for {'C': 0.05248074602497723, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.496 (+/-0.001) for {'C': 0.05248074602497723, 'kernel': 'rbf', 'gamma': 0.016811249744769604}
0.496 (+/-0.001) for {'C': 0.05248074602497723, 'kernel': 'rbf', 'gamma': 0.01687329890625804}
0.496 (+/-0.001) for {'C': 0.05248074602497723, 'kernel': 'rbf', 'gamma': 0.01693557708691519}
0.496 (+/-0.001) for {'C': 0.05248074602497723, 'kernel': 'rbf', 'gamma': 0.016998085132034966}
0.496 (+/-0.001) for {'C': 0.05248074602497723, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.747 (+/-0.502) for {'C': 0.5248074602497723, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.747 (+/-0.502) for {'C': 0.5248074602497723, 'kernel': 'rbf', 'gamma': 0.01650440985652279}
0.747 (+/-0.502) for {'C': 0.5248074602497723, 'kernel': 'rbf', 'gamma': 0.01656532649318018}
0.747 (+/-0.502) for {'C': 0.5248074602497723, 'kernel': 'rbf', 'gamma': 0.016626467968935365}
0.747 (+/-0.502) for {'C': 0.5248074602497723, 'kernel': 'rbf', 'gamma': 0.016687835113653904}
0.747 (+/-0.502) for {'C': 0.5248074602497723, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.747 (+/-0.502) for {'C': 0.5248074602497723, 'kernel': 'rbf', 'gamma': 0.016811249744769604}
0.747 (+/-0.502) for {'C': 0.5248074602497723, 'kernel': 'rbf', 'gamma': 0.01687329890625804}
0.747 (+/-0.502) for {'C': 0.5248074602497723, 'kernel': 'rbf', 'gamma': 0.01693557708691519}
0.747 (+/-0.502) for {'C': 0.5248074602497723, 'kernel': 'rbf', 'gamma': 0.016998085132034966}
0.747 (+/-0.502) for {'C': 0.5248074602497723, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.747 (+/-0.502) for {'C': 5.248074602497723, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.747 (+/-0.502) for {'C': 5.248074602497723, 'kernel': 'rbf', 'gamma': 0.01650440985652279}
0.747 (+/-0.502) for {'C': 5.248074602497723, 'kernel': 'rbf', 'gamma': 0.01656532649318018}
0.747 (+/-0.502) for {'C': 5.248074602497723, 'kernel': 'rbf', 'gamma': 0.016626467968935365}
0.747 (+/-0.502) for {'C': 5.248074602497723, 'kernel': 'rbf', 'gamma': 0.016687835113653904}
0.747 (+/-0.502) for {'C': 5.248074602497723, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.747 (+/-0.502) for {'C': 5.248074602497723, 'kernel': 'rbf', 'gamma': 0.016811249744769604}
0.747 (+/-0.502) for {'C': 5.248074602497723, 'kernel': 'rbf', 'gamma': 0.01687329890625804}
0.747 (+/-0.502) for {'C': 5.248074602497723, 'kernel': 'rbf', 'gamma': 0.01693557708691519}
0.747 (+/-0.502) for {'C': 5.248074602497723, 'kernel': 'rbf', 'gamma': 0.016998085132034966}
0.747 (+/-0.502) for {'C': 5.248074602497723, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.727 (+/-0.397) for {'C': 52.48074602497723, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.735 (+/-0.402) for {'C': 52.48074602497723, 'kernel': 'rbf', 'gamma': 0.01650440985652279}
0.735 (+/-0.402) for {'C': 52.48074602497723, 'kernel': 'rbf', 'gamma': 0.01656532649318018}
0.735 (+/-0.402) for {'C': 52.48074602497723, 'kernel': 'rbf', 'gamma': 0.016626467968935365}
0.735 (+/-0.402) for {'C': 52.48074602497723, 'kernel': 'rbf', 'gamma': 0.016687835113653904}
0.735 (+/-0.402) for {'C': 52.48074602497723, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.735 (+/-0.402) for {'C': 52.48074602497723, 'kernel': 'rbf', 'gamma': 0.016811249744769604}
0.735 (+/-0.402) for {'C': 52.48074602497723, 'kernel': 'rbf', 'gamma': 0.01687329890625804}
0.735 (+/-0.402) for {'C': 52.48074602497723, 'kernel': 'rbf', 'gamma': 0.01693557708691519}
0.735 (+/-0.402) for {'C': 52.48074602497723, 'kernel': 'rbf', 'gamma': 0.016998085132034966}
0.735 (+/-0.402) for {'C': 52.48074602497723, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.658 (+/-0.387) for {'C': 524.8074602497722, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.658 (+/-0.387) for {'C': 524.8074602497722, 'kernel': 'rbf', 'gamma': 0.01650440985652279}
0.658 (+/-0.387) for {'C': 524.8074602497722, 'kernel': 'rbf', 'gamma': 0.01656532649318018}
0.658 (+/-0.387) for {'C': 524.8074602497722, 'kernel': 'rbf', 'gamma': 0.016626467968935365}
0.658 (+/-0.387) for {'C': 524.8074602497722, 'kernel': 'rbf', 'gamma': 0.016687835113653904}
0.658 (+/-0.387) for {'C': 524.8074602497722, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.658 (+/-0.387) for {'C': 524.8074602497722, 'kernel': 'rbf', 'gamma': 0.016811249744769604}
0.658 (+/-0.387) for {'C': 524.8074602497722, 'kernel': 'rbf', 'gamma': 0.01687329890625804}
0.658 (+/-0.387) for {'C': 524.8074602497722, 'kernel': 'rbf', 'gamma': 0.01693557708691519}
0.658 (+/-0.387) for {'C': 524.8074602497722, 'kernel': 'rbf', 'gamma': 0.016998085132034966}
0.658 (+/-0.387) for {'C': 524.8074602497722, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.629 (+/-0.312) for {'C': 5248.074602497723, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.629 (+/-0.312) for {'C': 5248.074602497723, 'kernel': 'rbf', 'gamma': 0.01650440985652279}
0.629 (+/-0.312) for {'C': 5248.074602497723, 'kernel': 'rbf', 'gamma': 0.01656532649318018}
0.629 (+/-0.312) for {'C': 5248.074602497723, 'kernel': 'rbf', 'gamma': 0.016626467968935365}
0.629 (+/-0.312) for {'C': 5248.074602497723, 'kernel': 'rbf', 'gamma': 0.016687835113653904}
0.629 (+/-0.312) for {'C': 5248.074602497723, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.629 (+/-0.312) for {'C': 5248.074602497723, 'kernel': 'rbf', 'gamma': 0.016811249744769604}
0.629 (+/-0.312) for {'C': 5248.074602497723, 'kernel': 'rbf', 'gamma': 0.01687329890625804}
0.629 (+/-0.312) for {'C': 5248.074602497723, 'kernel': 'rbf', 'gamma': 0.01693557708691519}
0.629 (+/-0.312) for {'C': 5248.074602497723, 'kernel': 'rbf', 'gamma': 0.016998085132034966}
0.629 (+/-0.312) for {'C': 5248.074602497723, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.614 (+/-0.327) for {'C': 52480.74602497723, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.614 (+/-0.327) for {'C': 52480.74602497723, 'kernel': 'rbf', 'gamma': 0.01650440985652279}
0.614 (+/-0.327) for {'C': 52480.74602497723, 'kernel': 'rbf', 'gamma': 0.01656532649318018}
0.614 (+/-0.327) for {'C': 52480.74602497723, 'kernel': 'rbf', 'gamma': 0.016626467968935365}
0.614 (+/-0.327) for {'C': 52480.74602497723, 'kernel': 'rbf', 'gamma': 0.016687835113653904}
0.614 (+/-0.327) for {'C': 52480.74602497723, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.614 (+/-0.327) for {'C': 52480.74602497723, 'kernel': 'rbf', 'gamma': 0.016811249744769604}
0.614 (+/-0.327) for {'C': 52480.74602497723, 'kernel': 'rbf', 'gamma': 0.01687329890625804}
0.614 (+/-0.327) for {'C': 52480.74602497723, 'kernel': 'rbf', 'gamma': 0.01693557708691519}
0.614 (+/-0.327) for {'C': 52480.74602497723, 'kernel': 'rbf', 'gamma': 0.016998085132034966}
0.614 (+/-0.327) for {'C': 52480.74602497723, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.614 (+/-0.327) for {'C': 524807.4602497723, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.614 (+/-0.327) for {'C': 524807.4602497723, 'kernel': 'rbf', 'gamma': 0.01650440985652279}
0.614 (+/-0.327) for {'C': 524807.4602497723, 'kernel': 'rbf', 'gamma': 0.01656532649318018}
0.614 (+/-0.327) for {'C': 524807.4602497723, 'kernel': 'rbf', 'gamma': 0.016626467968935365}
0.614 (+/-0.327) for {'C': 524807.4602497723, 'kernel': 'rbf', 'gamma': 0.016687835113653904}
0.614 (+/-0.327) for {'C': 524807.4602497723, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.614 (+/-0.327) for {'C': 524807.4602497723, 'kernel': 'rbf', 'gamma': 0.016811249744769604}
0.614 (+/-0.327) for {'C': 524807.4602497723, 'kernel': 'rbf', 'gamma': 0.01687329890625804}
0.614 (+/-0.327) for {'C': 524807.4602497723, 'kernel': 'rbf', 'gamma': 0.01693557708691519}
0.614 (+/-0.327) for {'C': 524807.4602497723, 'kernel': 'rbf', 'gamma': 0.016998085132034966}
0.614 (+/-0.327) for {'C': 524807.4602497723, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.614 (+/-0.327) for {'C': 5248074.602497723, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.614 (+/-0.327) for {'C': 5248074.602497723, 'kernel': 'rbf', 'gamma': 0.01650440985652279}
0.614 (+/-0.327) for {'C': 5248074.602497723, 'kernel': 'rbf', 'gamma': 0.01656532649318018}
0.614 (+/-0.327) for {'C': 5248074.602497723, 'kernel': 'rbf', 'gamma': 0.016626467968935365}
0.614 (+/-0.327) for {'C': 5248074.602497723, 'kernel': 'rbf', 'gamma': 0.016687835113653904}
0.614 (+/-0.327) for {'C': 5248074.602497723, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.614 (+/-0.327) for {'C': 5248074.602497723, 'kernel': 'rbf', 'gamma': 0.016811249744769604}
0.614 (+/-0.327) for {'C': 5248074.602497723, 'kernel': 'rbf', 'gamma': 0.01687329890625804}
0.614 (+/-0.327) for {'C': 5248074.602497723, 'kernel': 'rbf', 'gamma': 0.01693557708691519}
0.614 (+/-0.327) for {'C': 5248074.602497723, 'kernel': 'rbf', 'gamma': 0.016998085132034966}
0.614 (+/-0.327) for {'C': 5248074.602497723, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 1.0, 'kernel': 'linear'}
0.673 (+/-0.377) for {'C': 10.0, 'kernel': 'linear'}
0.631 (+/-0.312) for {'C': 100.0, 'kernel': 'linear'}
0.653 (+/-0.293) for {'C': 1000.0, 'kernel': 'linear'}
0.619 (+/-0.322) for {'C': 10000.0, 'kernel': 'linear'}
0.619 (+/-0.322) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.20 0.17 0.18 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.500 (+/-0.000) for {'C': 0.0005248074602497723, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.500 (+/-0.000) for {'C': 0.0005248074602497723, 'kernel': 'rbf', 'gamma': 0.01650440985652279}
0.500 (+/-0.000) for {'C': 0.0005248074602497723, 'kernel': 'rbf', 'gamma': 0.01656532649318018}
0.500 (+/-0.000) for {'C': 0.0005248074602497723, 'kernel': 'rbf', 'gamma': 0.016626467968935365}
0.500 (+/-0.000) for {'C': 0.0005248074602497723, 'kernel': 'rbf', 'gamma': 0.016687835113653904}
0.500 (+/-0.000) for {'C': 0.0005248074602497723, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.500 (+/-0.000) for {'C': 0.0005248074602497723, 'kernel': 'rbf', 'gamma': 0.016811249744769604}
0.500 (+/-0.000) for {'C': 0.0005248074602497723, 'kernel': 'rbf', 'gamma': 0.01687329890625804}
0.500 (+/-0.000) for {'C': 0.0005248074602497723, 'kernel': 'rbf', 'gamma': 0.01693557708691519}
0.500 (+/-0.000) for {'C': 0.0005248074602497723, 'kernel': 'rbf', 'gamma': 0.016998085132034966}
0.500 (+/-0.000) for {'C': 0.0005248074602497723, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.500 (+/-0.000) for {'C': 0.005248074602497723, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.500 (+/-0.000) for {'C': 0.005248074602497723, 'kernel': 'rbf', 'gamma': 0.01650440985652279}
0.500 (+/-0.000) for {'C': 0.005248074602497723, 'kernel': 'rbf', 'gamma': 0.01656532649318018}
0.500 (+/-0.000) for {'C': 0.005248074602497723, 'kernel': 'rbf', 'gamma': 0.016626467968935365}
0.500 (+/-0.000) for {'C': 0.005248074602497723, 'kernel': 'rbf', 'gamma': 0.016687835113653904}
0.500 (+/-0.000) for {'C': 0.005248074602497723, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.500 (+/-0.000) for {'C': 0.005248074602497723, 'kernel': 'rbf', 'gamma': 0.016811249744769604}
0.500 (+/-0.000) for {'C': 0.005248074602497723, 'kernel': 'rbf', 'gamma': 0.01687329890625804}
0.500 (+/-0.000) for {'C': 0.005248074602497723, 'kernel': 'rbf', 'gamma': 0.01693557708691519}
0.500 (+/-0.000) for {'C': 0.005248074602497723, 'kernel': 'rbf', 'gamma': 0.016998085132034966}
0.500 (+/-0.000) for {'C': 0.005248074602497723, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.500 (+/-0.000) for {'C': 0.05248074602497723, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.500 (+/-0.000) for {'C': 0.05248074602497723, 'kernel': 'rbf', 'gamma': 0.01650440985652279}
0.500 (+/-0.000) for {'C': 0.05248074602497723, 'kernel': 'rbf', 'gamma': 0.01656532649318018}
0.500 (+/-0.000) for {'C': 0.05248074602497723, 'kernel': 'rbf', 'gamma': 0.016626467968935365}
0.500 (+/-0.000) for {'C': 0.05248074602497723, 'kernel': 'rbf', 'gamma': 0.016687835113653904}
0.500 (+/-0.000) for {'C': 0.05248074602497723, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.500 (+/-0.000) for {'C': 0.05248074602497723, 'kernel': 'rbf', 'gamma': 0.016811249744769604}
0.500 (+/-0.000) for {'C': 0.05248074602497723, 'kernel': 'rbf', 'gamma': 0.01687329890625804}
0.500 (+/-0.000) for {'C': 0.05248074602497723, 'kernel': 'rbf', 'gamma': 0.01693557708691519}
0.500 (+/-0.000) for {'C': 0.05248074602497723, 'kernel': 'rbf', 'gamma': 0.016998085132034966}
0.500 (+/-0.000) for {'C': 0.05248074602497723, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.617 (+/-0.238) for {'C': 0.5248074602497723, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.617 (+/-0.238) for {'C': 0.5248074602497723, 'kernel': 'rbf', 'gamma': 0.01650440985652279}
0.617 (+/-0.238) for {'C': 0.5248074602497723, 'kernel': 'rbf', 'gamma': 0.01656532649318018}
0.617 (+/-0.238) for {'C': 0.5248074602497723, 'kernel': 'rbf', 'gamma': 0.016626467968935365}
0.617 (+/-0.238) for {'C': 0.5248074602497723, 'kernel': 'rbf', 'gamma': 0.016687835113653904}
0.617 (+/-0.238) for {'C': 0.5248074602497723, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.617 (+/-0.238) for {'C': 0.5248074602497723, 'kernel': 'rbf', 'gamma': 0.016811249744769604}
0.617 (+/-0.238) for {'C': 0.5248074602497723, 'kernel': 'rbf', 'gamma': 0.01687329890625804}
0.617 (+/-0.238) for {'C': 0.5248074602497723, 'kernel': 'rbf', 'gamma': 0.01693557708691519}
0.617 (+/-0.238) for {'C': 0.5248074602497723, 'kernel': 'rbf', 'gamma': 0.016998085132034966}
0.617 (+/-0.238) for {'C': 0.5248074602497723, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.617 (+/-0.238) for {'C': 5.248074602497723, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.617 (+/-0.238) for {'C': 5.248074602497723, 'kernel': 'rbf', 'gamma': 0.01650440985652279}
0.617 (+/-0.238) for {'C': 5.248074602497723, 'kernel': 'rbf', 'gamma': 0.01656532649318018}
0.617 (+/-0.238) for {'C': 5.248074602497723, 'kernel': 'rbf', 'gamma': 0.016626467968935365}
0.617 (+/-0.238) for {'C': 5.248074602497723, 'kernel': 'rbf', 'gamma': 0.016687835113653904}
0.617 (+/-0.238) for {'C': 5.248074602497723, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.617 (+/-0.238) for {'C': 5.248074602497723, 'kernel': 'rbf', 'gamma': 0.016811249744769604}
0.617 (+/-0.238) for {'C': 5.248074602497723, 'kernel': 'rbf', 'gamma': 0.01687329890625804}
0.617 (+/-0.238) for {'C': 5.248074602497723, 'kernel': 'rbf', 'gamma': 0.01693557708691519}
0.617 (+/-0.238) for {'C': 5.248074602497723, 'kernel': 'rbf', 'gamma': 0.016998085132034966}
0.617 (+/-0.238) for {'C': 5.248074602497723, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.682 (+/-0.294) for {'C': 52.48074602497723, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.682 (+/-0.295) for {'C': 52.48074602497723, 'kernel': 'rbf', 'gamma': 0.01650440985652279}
0.682 (+/-0.295) for {'C': 52.48074602497723, 'kernel': 'rbf', 'gamma': 0.01656532649318018}
0.682 (+/-0.295) for {'C': 52.48074602497723, 'kernel': 'rbf', 'gamma': 0.016626467968935365}
0.682 (+/-0.295) for {'C': 52.48074602497723, 'kernel': 'rbf', 'gamma': 0.016687835113653904}
0.682 (+/-0.295) for {'C': 52.48074602497723, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.682 (+/-0.295) for {'C': 52.48074602497723, 'kernel': 'rbf', 'gamma': 0.016811249744769604}
0.682 (+/-0.295) for {'C': 52.48074602497723, 'kernel': 'rbf', 'gamma': 0.01687329890625804}
0.682 (+/-0.295) for {'C': 52.48074602497723, 'kernel': 'rbf', 'gamma': 0.01693557708691519}
0.682 (+/-0.295) for {'C': 52.48074602497723, 'kernel': 'rbf', 'gamma': 0.016998085132034966}
0.682 (+/-0.295) for {'C': 52.48074602497723, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.658 (+/-0.314) for {'C': 524.8074602497722, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.658 (+/-0.314) for {'C': 524.8074602497722, 'kernel': 'rbf', 'gamma': 0.01650440985652279}
0.658 (+/-0.314) for {'C': 524.8074602497722, 'kernel': 'rbf', 'gamma': 0.01656532649318018}
0.658 (+/-0.314) for {'C': 524.8074602497722, 'kernel': 'rbf', 'gamma': 0.016626467968935365}
0.658 (+/-0.314) for {'C': 524.8074602497722, 'kernel': 'rbf', 'gamma': 0.016687835113653904}
0.658 (+/-0.314) for {'C': 524.8074602497722, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.658 (+/-0.314) for {'C': 524.8074602497722, 'kernel': 'rbf', 'gamma': 0.016811249744769604}
0.658 (+/-0.315) for {'C': 524.8074602497722, 'kernel': 'rbf', 'gamma': 0.01687329890625804}
0.658 (+/-0.315) for {'C': 524.8074602497722, 'kernel': 'rbf', 'gamma': 0.01693557708691519}
0.658 (+/-0.315) for {'C': 524.8074602497722, 'kernel': 'rbf', 'gamma': 0.016998085132034966}
0.658 (+/-0.314) for {'C': 524.8074602497722, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.637 (+/-0.237) for {'C': 5248.074602497723, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.637 (+/-0.237) for {'C': 5248.074602497723, 'kernel': 'rbf', 'gamma': 0.01650440985652279}
0.637 (+/-0.237) for {'C': 5248.074602497723, 'kernel': 'rbf', 'gamma': 0.01656532649318018}
0.637 (+/-0.237) for {'C': 5248.074602497723, 'kernel': 'rbf', 'gamma': 0.016626467968935365}
0.637 (+/-0.237) for {'C': 5248.074602497723, 'kernel': 'rbf', 'gamma': 0.016687835113653904}
0.637 (+/-0.237) for {'C': 5248.074602497723, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.637 (+/-0.237) for {'C': 5248.074602497723, 'kernel': 'rbf', 'gamma': 0.016811249744769604}
0.637 (+/-0.237) for {'C': 5248.074602497723, 'kernel': 'rbf', 'gamma': 0.01687329890625804}
0.637 (+/-0.237) for {'C': 5248.074602497723, 'kernel': 'rbf', 'gamma': 0.01693557708691519}
0.637 (+/-0.237) for {'C': 5248.074602497723, 'kernel': 'rbf', 'gamma': 0.016998085132034966}
0.637 (+/-0.237) for {'C': 5248.074602497723, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.588 (+/-0.232) for {'C': 52480.74602497723, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.588 (+/-0.232) for {'C': 52480.74602497723, 'kernel': 'rbf', 'gamma': 0.01650440985652279}
0.588 (+/-0.232) for {'C': 52480.74602497723, 'kernel': 'rbf', 'gamma': 0.01656532649318018}
0.588 (+/-0.232) for {'C': 52480.74602497723, 'kernel': 'rbf', 'gamma': 0.016626467968935365}
0.588 (+/-0.232) for {'C': 52480.74602497723, 'kernel': 'rbf', 'gamma': 0.016687835113653904}
0.588 (+/-0.232) for {'C': 52480.74602497723, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.588 (+/-0.232) for {'C': 52480.74602497723, 'kernel': 'rbf', 'gamma': 0.016811249744769604}
0.588 (+/-0.232) for {'C': 52480.74602497723, 'kernel': 'rbf', 'gamma': 0.01687329890625804}
0.588 (+/-0.232) for {'C': 52480.74602497723, 'kernel': 'rbf', 'gamma': 0.01693557708691519}
0.588 (+/-0.232) for {'C': 52480.74602497723, 'kernel': 'rbf', 'gamma': 0.016998085132034966}
0.588 (+/-0.232) for {'C': 52480.74602497723, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.587 (+/-0.234) for {'C': 524807.4602497723, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.587 (+/-0.234) for {'C': 524807.4602497723, 'kernel': 'rbf', 'gamma': 0.01650440985652279}
0.587 (+/-0.234) for {'C': 524807.4602497723, 'kernel': 'rbf', 'gamma': 0.01656532649318018}
0.587 (+/-0.234) for {'C': 524807.4602497723, 'kernel': 'rbf', 'gamma': 0.016626467968935365}
0.587 (+/-0.234) for {'C': 524807.4602497723, 'kernel': 'rbf', 'gamma': 0.016687835113653904}
0.587 (+/-0.234) for {'C': 524807.4602497723, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.587 (+/-0.234) for {'C': 524807.4602497723, 'kernel': 'rbf', 'gamma': 0.016811249744769604}
0.587 (+/-0.234) for {'C': 524807.4602497723, 'kernel': 'rbf', 'gamma': 0.01687329890625804}
0.587 (+/-0.234) for {'C': 524807.4602497723, 'kernel': 'rbf', 'gamma': 0.01693557708691519}
0.587 (+/-0.234) for {'C': 524807.4602497723, 'kernel': 'rbf', 'gamma': 0.016998085132034966}
0.587 (+/-0.234) for {'C': 524807.4602497723, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.587 (+/-0.234) for {'C': 5248074.602497723, 'kernel': 'rbf', 'gamma': 0.016443717232149314}
0.587 (+/-0.234) for {'C': 5248074.602497723, 'kernel': 'rbf', 'gamma': 0.01650440985652279}
0.587 (+/-0.234) for {'C': 5248074.602497723, 'kernel': 'rbf', 'gamma': 0.01656532649318018}
0.587 (+/-0.234) for {'C': 5248074.602497723, 'kernel': 'rbf', 'gamma': 0.016626467968935365}
0.587 (+/-0.234) for {'C': 5248074.602497723, 'kernel': 'rbf', 'gamma': 0.016687835113653904}
0.587 (+/-0.234) for {'C': 5248074.602497723, 'kernel': 'rbf', 'gamma': 0.016749428760264376}
0.587 (+/-0.234) for {'C': 5248074.602497723, 'kernel': 'rbf', 'gamma': 0.016811249744769604}
0.587 (+/-0.234) for {'C': 5248074.602497723, 'kernel': 'rbf', 'gamma': 0.01687329890625804}
0.587 (+/-0.234) for {'C': 5248074.602497723, 'kernel': 'rbf', 'gamma': 0.01693557708691519}
0.587 (+/-0.234) for {'C': 5248074.602497723, 'kernel': 'rbf', 'gamma': 0.016998085132034966}
0.587 (+/-0.234) for {'C': 5248074.602497723, 'kernel': 'rbf', 'gamma': 0.017060823890031242}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.663 (+/-0.315) for {'C': 10.0, 'kernel': 'linear'}
0.661 (+/-0.315) for {'C': 100.0, 'kernel': 'linear'}
0.662 (+/-0.227) for {'C': 1000.0, 'kernel': 'linear'}
0.611 (+/-0.241) for {'C': 10000.0, 'kernel': 'linear'}
0.611 (+/-0.240) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.17 0.17 0.17 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 52.48074602497723, 'kernel': 'rbf', 'gamma': 0.01650440985652279}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6822115384615385
第2轮loop中,tunemodel函数得到的最优参数是: ([{'C': array([ 5.2480746 , 8.31763771, 13.18256739, 20.89296131,
33.11311215, 52.48074602, 83.17637711, 131.82567386,
208.92961309, 331.13112148, 524.80746025]), 'kernel': ['rbf'], 'gamma': array([0.01644372, 0.01645584, 0.01646797, 0.01648011, 0.01649225,
0.01650441, 0.01651658, 0.01652875, 0.01654093, 0.01655313,
0.01656533])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}], {'C': 52.48074602497723, 'kernel': 'rbf', 'gamma': 0.01650440985652279}, 0.6822115384615385)
这是第2次迭代微调C和gamma。
第2次迭代,得到delta: [3.55271368e-14 2.45018904e-04]
SVC模型clf_op_iter的参数是: {'kernel': 'rbf', 'decision_function_shape': 'ovr', 'class_weight': {-1: 15}, 'tol': 0.001, 'gamma': 0.01650440985652279, 'random_state': 0, 'cache_size': 200, 'verbose': False, 'degree': 3, 'C': 52.48074602497723, 'probability': False, 'shrinking': True, 'coef0': 0.0, 'max_iter': -1}
训练模型SVC对训练样本的预测准确率: 0.9468462083628633
测试集中,预测为舞弊样本的有: (array([ 8, 10, 11, 12, 13, 14, 19, 20, 21, 22, 24,
25, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39,
40, 41, 42, 43, 44, 47, 48, 49, 50, 51, 52,
53, 54, 58, 59, 60, 64, 65, 66, 67, 68, 71,
72, 73, 74, 75, 76, 77, 78, 79, 88, 90, 91,
92, 93, 98, 101, 107, 108, 109, 110, 111, 112, 113,
114, 115, 116, 117, 118, 119, 121, 127, 129, 135, 136,
142, 143, 144, 145, 146, 150, 151, 153, 156, 157, 158,
163, 164, 165, 166, 176, 177, 180, 181, 182, 183, 184,
185, 186, 187, 192, 193, 197, 199, 200, 201, 202, 204,
207, 208, 212, 213, 214, 215, 216, 219, 225, 231, 232,
233, 234, 235, 236, 237, 238, 239, 242, 243, 244, 245,
246, 247, 251, 252, 253, 254, 255, 256, 257, 258, 259,
260, 261, 262, 264, 268, 269, 270, 275, 276, 277, 278,
279, 280, 281, 283, 284, 285, 286, 287, 288, 299, 300,
301, 302, 306, 313, 316, 317, 319, 322, 323, 324, 325,
330, 331, 332, 333, 334, 338, 339, 340, 346, 347, 348,
349, 351, 352, 353, 354, 356, 358, 359, 360, 361, 362,
363, 364, 365, 366, 367, 368, 369, 370, 379, 383, 384,
385, 386, 388, 391, 392, 393, 394, 395, 396, 398, 399,
403, 404, 410, 414, 419, 420, 421, 422, 423, 426, 427,
429, 430, 431, 432, 433, 434, 438, 439, 440, 441, 442,
443, 444, 447, 449, 454, 455, 456, 457, 461, 463, 465,
466, 467, 468, 469, 471, 472, 473, 474, 475, 476, 477,
478, 479, 481, 482, 483, 484, 485, 486, 487, 488, 489,
490, 492, 496, 498, 499, 500, 501, 502, 503, 509, 513,
514, 515, 516, 518, 519, 520, 521, 529, 530, 532, 534,
535, 540, 541, 543, 544, 545, 546, 547, 549, 555, 557,
558, 562, 563, 564, 565, 566, 567, 568, 571, 572, 573,
582, 583, 584, 586, 587, 589, 590, 593, 595, 600, 601,
604, 605, 611, 612, 613, 614, 615, 616, 617, 618, 619,
624, 629, 630, 633, 634, 635, 641, 642, 643, 651, 653,
656, 657, 658, 661, 662, 664, 665, 666, 667, 668, 669,
674, 675, 676, 677, 680, 684, 685, 686, 687, 688, 691,
692, 693, 694, 695, 696, 697, 699, 701, 702, 706, 716,
717, 718, 720, 721, 722, 723, 724, 729, 735, 736, 742,
743, 752, 753, 754, 756, 758, 759, 760, 761, 769, 774,
775, 776, 777, 778, 786, 787, 788, 789, 790, 791, 792,
793, 794, 795, 796, 797, 807, 813, 814, 815, 816, 817,
819, 820, 825, 826, 827, 828, 829, 830, 840, 841, 843,
844, 845, 848, 851, 852, 853, 854, 855, 856, 857, 859,
862, 864, 872, 874, 875, 876, 877, 878, 879, 880, 881,
882, 883, 884, 885, 897, 898, 902, 903, 904, 905, 906,
907, 910, 911, 912, 913, 914, 916, 923, 928, 929, 930,
931, 932, 933, 934, 935, 948, 949, 950, 951, 952, 953,
954, 955, 956, 959, 960, 961, 963, 964, 965, 966, 967,
971, 976, 977, 978, 979, 980, 981, 982, 983, 984, 985,
998, 1000, 1002, 1003, 1004, 1005, 1006, 1008, 1009, 1012, 1013,
1014, 1015, 1016, 1017, 1020, 1021, 1022, 1030, 1032, 1033, 1034,
1037, 1040, 1041, 1048, 1049, 1050, 1051, 1056, 1057, 1060, 1061,
1064, 1065, 1067, 1068, 1071, 1073, 1074, 1080, 1081, 1082, 1085,
1086, 1089, 1090, 1092, 1094, 1095, 1096, 1097, 1098, 1099, 1100,
1101, 1106, 1107, 1112, 1113, 1114, 1119, 1121, 1122, 1123, 1124,
1127, 1129, 1131, 1132, 1134, 1135, 1137, 1140, 1141, 1142, 1144,
1148, 1152, 1154, 1156, 1158, 1160, 1163, 1164, 1167, 1168, 1171,
1175, 1180, 1181, 1183, 1188, 1191, 1196, 1205, 1211, 1215, 1217,
1218, 1219, 1222, 1227, 1230, 1231, 1232, 1233, 1234, 1235, 1236,
1237, 1238, 1239, 1240, 1241, 1242, 1243, 1244, 1246, 1247, 1248,
1249, 1250, 1251, 1252, 1254, 1255, 1256], dtype=int64),)
测试集中,实际为舞弊样本的有: (array([1246, 1247, 1248, 1249, 1250, 1251, 1252, 1253, 1254, 1255, 1256],
dtype=int64),)
预测的舞弊样本数目: 645
训练模型SVC对测试样本的预测准确率: 0.5393338058114813
以上是第30次特征筛选。
第30次特征筛选,AUC值是: 0.6997300452356633
X_train_iter_svc.shape is: (1257, 22)
X_test_iter_svc.shape is: (1257, 22)
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.647 (+/-0.401) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.747 (+/-0.449) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.17 0.17 0.17 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.589 (+/-0.232) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.641 (+/-0.236) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.17 0.17 0.17 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6413461538461539
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.748 (+/-0.449) for {'C': 1.0, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.637 (+/-0.307) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.20 0.17 0.18 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.637 (+/-0.239) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.20 0.17 0.18 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.99 0.99 629
本轮grid search结果,得到最好的参数选择是: {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6663461538461538
RBF的粗grid search最佳超参: {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001} RBF的粗grid search最高分值: 0.6413461538461539
linear的粗grid search最佳超参: {'C': 1.0, 'kernel': 'linear'} linear的粗grid search最高分值: 0.6663461538461538
粗grid search得到的parameter是:
[{'C': array([1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02, 1.e+03, 1.e+04, 1.e+05,
1.e+06, 1.e+07, 1.e+08]), 'kernel': ['rbf'], 'gamma': array([1.e-08, 1.e-07, 1.e-06, 1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01,
1.e+00, 1.e+01, 1.e+02])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.647 (+/-0.333) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.449) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.319) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.654 (+/-0.392) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.449) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.375) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.647 (+/-0.401) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.748 (+/-0.449) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.375) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.630 (+/-0.311) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.647 (+/-0.401) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.747 (+/-0.502) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.748 (+/-0.449) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.706 (+/-0.383) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.629 (+/-0.312) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.647 (+/-0.401) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.747 (+/-0.502) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.748 (+/-0.449) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.706 (+/-0.383) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.630 (+/-0.311) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.621 (+/-0.320) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.647 (+/-0.401) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.649 (+/-0.778) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.772 (+/-0.473) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.668 (+/-0.290) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.637 (+/-0.310) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.637 (+/-0.307) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.614 (+/-0.327) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.647 (+/-0.401) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.699 (+/-0.797) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.764 (+/-0.478) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.659 (+/-0.313) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.629 (+/-0.312) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.633 (+/-0.310) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.614 (+/-0.327) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.647 (+/-0.401) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'linear'}
0.706 (+/-0.383) for {'C': 10.0, 'kernel': 'linear'}
0.643 (+/-0.320) for {'C': 100.0, 'kernel': 'linear'}
0.637 (+/-0.307) for {'C': 1000.0, 'kernel': 'linear'}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'linear'}
0.620 (+/-0.321) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.006) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.004) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.641 (+/-0.236) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.239) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.004) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.641 (+/-0.236) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.641 (+/-0.236) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.587 (+/-0.233) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.589 (+/-0.232) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.004) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.316) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.641 (+/-0.236) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.637 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.588 (+/-0.232) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.589 (+/-0.232) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.004) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.617 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.666 (+/-0.316) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.316) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.636 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.588 (+/-0.232) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.588 (+/-0.232) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.589 (+/-0.232) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.004) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.617 (+/-0.238) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.666 (+/-0.316) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.665 (+/-0.317) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.637 (+/-0.238) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.612 (+/-0.241) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.586 (+/-0.235) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.588 (+/-0.232) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.589 (+/-0.232) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.004) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.617 (+/-0.238) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.642 (+/-0.236) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.681 (+/-0.294) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.661 (+/-0.319) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.638 (+/-0.238) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.586 (+/-0.236) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.586 (+/-0.235) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.588 (+/-0.232) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.589 (+/-0.232) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.004) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.642 (+/-0.236) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.641 (+/-0.236) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.664 (+/-0.316) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.660 (+/-0.315) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.636 (+/-0.240) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.586 (+/-0.236) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.586 (+/-0.235) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.588 (+/-0.232) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.589 (+/-0.232) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.004) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'linear'}
0.666 (+/-0.315) for {'C': 10.0, 'kernel': 'linear'}
0.637 (+/-0.237) for {'C': 100.0, 'kernel': 'linear'}
0.637 (+/-0.239) for {'C': 1000.0, 'kernel': 'linear'}
0.585 (+/-0.236) for {'C': 10000.0, 'kernel': 'linear'}
0.611 (+/-0.242) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.11 0.17 0.13 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6812489694121527
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.673 (+/-0.330) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.627 (+/-0.330) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.714 (+/-0.359) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.609 (+/-0.313) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.654 (+/-0.392) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.714 (+/-0.359) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.658 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.647 (+/-0.401) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.714 (+/-0.359) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.658 (+/-0.388) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.630 (+/-0.311) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.647 (+/-0.401) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.747 (+/-0.502) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.797 (+/-0.492) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.677 (+/-0.374) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.658 (+/-0.387) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.629 (+/-0.312) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.647 (+/-0.401) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.024) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.747 (+/-0.502) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.660 (+/-0.290) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.658 (+/-0.388) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.630 (+/-0.311) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.621 (+/-0.320) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.647 (+/-0.401) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.699 (+/-0.797) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.710 (+/-0.420) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.637 (+/-0.291) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.630 (+/-0.310) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.637 (+/-0.307) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.614 (+/-0.327) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.647 (+/-0.401) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.650 (+/-0.777) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.662 (+/-0.373) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.625 (+/-0.315) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.628 (+/-0.312) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.633 (+/-0.310) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.614 (+/-0.327) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.647 (+/-0.401) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 1.0, 'kernel': 'linear'}
0.674 (+/-0.375) for {'C': 10.0, 'kernel': 'linear'}
0.631 (+/-0.312) for {'C': 100.0, 'kernel': 'linear'}
0.637 (+/-0.307) for {'C': 1000.0, 'kernel': 'linear'}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'linear'}
0.620 (+/-0.321) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.006) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.666 (+/-0.315) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.611 (+/-0.242) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.004) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.682 (+/-0.295) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.608 (+/-0.244) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.239) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.004) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.682 (+/-0.295) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.658 (+/-0.314) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.587 (+/-0.233) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.589 (+/-0.232) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.004) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.682 (+/-0.295) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.658 (+/-0.314) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.637 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.588 (+/-0.232) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.589 (+/-0.232) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.004) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.617 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.642 (+/-0.236) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.665 (+/-0.314) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.658 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.636 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.588 (+/-0.232) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.588 (+/-0.232) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.589 (+/-0.232) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.004) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.523 (+/-0.138) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.617 (+/-0.238) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.681 (+/-0.293) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.658 (+/-0.315) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.637 (+/-0.238) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.612 (+/-0.241) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.586 (+/-0.235) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.588 (+/-0.232) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.589 (+/-0.232) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.004) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.642 (+/-0.236) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.641 (+/-0.235) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.677 (+/-0.295) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.660 (+/-0.317) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.638 (+/-0.238) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.586 (+/-0.236) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.586 (+/-0.235) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.588 (+/-0.232) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.589 (+/-0.232) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.004) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.651 (+/-0.258) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.640 (+/-0.233) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.658 (+/-0.315) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.660 (+/-0.314) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.636 (+/-0.240) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.586 (+/-0.236) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.586 (+/-0.235) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.588 (+/-0.232) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.589 (+/-0.232) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.004) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.663 (+/-0.316) for {'C': 10.0, 'kernel': 'linear'}
0.661 (+/-0.315) for {'C': 100.0, 'kernel': 'linear'}
0.637 (+/-0.239) for {'C': 1000.0, 'kernel': 'linear'}
0.585 (+/-0.236) for {'C': 10000.0, 'kernel': 'linear'}
0.611 (+/-0.242) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.17 0.17 0.17 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6822115384615385
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.496 (+/-0.001) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.747 (+/-0.502) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.747 (+/-0.502) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.747 (+/-0.502) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.747 (+/-0.502) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.747 (+/-0.502) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.747 (+/-0.502) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.747 (+/-0.502) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.747 (+/-0.502) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.697 (+/-0.437) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.747 (+/-0.502) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.747 (+/-0.502) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.747 (+/-0.502) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.747 (+/-0.502) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.747 (+/-0.502) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.747 (+/-0.502) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.747 (+/-0.502) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.747 (+/-0.449) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.714 (+/-0.418) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.747 (+/-0.502) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.747 (+/-0.502) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.747 (+/-0.502) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.747 (+/-0.502) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.747 (+/-0.502) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.747 (+/-0.502) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.747 (+/-0.449) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.723 (+/-0.423) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.664 (+/-0.388) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.747 (+/-0.502) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.747 (+/-0.502) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.747 (+/-0.502) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.747 (+/-0.502) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.747 (+/-0.502) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.747 (+/-0.449) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.723 (+/-0.423) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.664 (+/-0.388) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.631 (+/-0.319) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.747 (+/-0.502) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.747 (+/-0.502) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.747 (+/-0.502) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.747 (+/-0.502) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.747 (+/-0.449) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.723 (+/-0.423) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.698 (+/-0.383) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.664 (+/-0.388) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.664 (+/-0.388) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.747 (+/-0.502) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.747 (+/-0.502) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.747 (+/-0.502) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.747 (+/-0.449) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.723 (+/-0.423) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.723 (+/-0.423) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.664 (+/-0.388) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.672 (+/-0.392) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.631 (+/-0.319) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.747 (+/-0.502) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.722 (+/-0.473) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.747 (+/-0.449) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.723 (+/-0.423) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.723 (+/-0.423) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.689 (+/-0.374) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.672 (+/-0.392) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.664 (+/-0.388) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.631 (+/-0.319) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.722 (+/-0.473) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.747 (+/-0.449) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.723 (+/-0.423) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.723 (+/-0.423) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.698 (+/-0.383) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.697 (+/-0.375) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.672 (+/-0.392) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.614 (+/-0.327) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.614 (+/-0.327) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.001}
0.722 (+/-0.473) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.747 (+/-0.449) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.723 (+/-0.423) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.723 (+/-0.423) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.698 (+/-0.383) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.697 (+/-0.375) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.672 (+/-0.392) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.622 (+/-0.336) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.614 (+/-0.327) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.614 (+/-0.327) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.449) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.723 (+/-0.423) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.723 (+/-0.423) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.698 (+/-0.383) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.697 (+/-0.375) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.672 (+/-0.392) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.657 (+/-0.390) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.621 (+/-0.320) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.614 (+/-0.327) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.614 (+/-0.327) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.449) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.723 (+/-0.423) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.723 (+/-0.423) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.698 (+/-0.383) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.706 (+/-0.383) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.697 (+/-0.375) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.657 (+/-0.390) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.621 (+/-0.320) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.619 (+/-0.322) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.614 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.614 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'linear'}
0.706 (+/-0.383) for {'C': 10.0, 'kernel': 'linear'}
0.643 (+/-0.320) for {'C': 100.0, 'kernel': 'linear'}
0.637 (+/-0.307) for {'C': 1000.0, 'kernel': 'linear'}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'linear'}
0.620 (+/-0.321) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.25 0.17 0.20 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.500 (+/-0.000) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.617 (+/-0.238) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.617 (+/-0.238) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.617 (+/-0.238) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.617 (+/-0.238) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.617 (+/-0.238) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.617 (+/-0.238) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.617 (+/-0.238) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.617 (+/-0.238) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.616 (+/-0.237) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.617 (+/-0.238) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.617 (+/-0.238) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.617 (+/-0.238) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.617 (+/-0.238) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.617 (+/-0.238) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.617 (+/-0.238) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.617 (+/-0.238) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.641 (+/-0.236) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.641 (+/-0.235) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.617 (+/-0.238) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.617 (+/-0.238) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.617 (+/-0.238) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.617 (+/-0.238) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.617 (+/-0.238) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.617 (+/-0.238) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.641 (+/-0.236) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.666 (+/-0.315) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.616 (+/-0.237) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.617 (+/-0.238) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.617 (+/-0.238) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.617 (+/-0.238) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.617 (+/-0.238) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.617 (+/-0.238) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.641 (+/-0.236) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.666 (+/-0.315) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.616 (+/-0.237) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.616 (+/-0.236) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.617 (+/-0.238) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.617 (+/-0.238) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.617 (+/-0.238) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.617 (+/-0.238) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.641 (+/-0.236) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.666 (+/-0.315) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.666 (+/-0.315) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.616 (+/-0.237) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.616 (+/-0.237) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.617 (+/-0.238) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.617 (+/-0.238) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.617 (+/-0.238) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.641 (+/-0.236) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.666 (+/-0.315) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.666 (+/-0.315) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.616 (+/-0.237) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.616 (+/-0.238) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.615 (+/-0.237) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.617 (+/-0.238) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.617 (+/-0.238) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.641 (+/-0.236) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.666 (+/-0.315) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.666 (+/-0.315) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.641 (+/-0.235) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.616 (+/-0.238) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.616 (+/-0.238) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.615 (+/-0.237) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.617 (+/-0.238) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.641 (+/-0.236) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.666 (+/-0.315) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.666 (+/-0.315) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.666 (+/-0.315) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.641 (+/-0.236) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.616 (+/-0.238) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.590 (+/-0.229) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.588 (+/-0.233) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.641 (+/-0.236) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.666 (+/-0.315) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.666 (+/-0.315) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.666 (+/-0.315) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.641 (+/-0.236) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.616 (+/-0.238) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.590 (+/-0.230) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.588 (+/-0.232) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.587 (+/-0.234) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.001}
0.641 (+/-0.236) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.666 (+/-0.315) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.666 (+/-0.315) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.666 (+/-0.315) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.641 (+/-0.236) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.616 (+/-0.238) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.615 (+/-0.238) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.613 (+/-0.238) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.587 (+/-0.234) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.587 (+/-0.234) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.1}
0.641 (+/-0.236) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.315) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.666 (+/-0.315) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.666 (+/-0.315) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.666 (+/-0.315) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.641 (+/-0.236) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.615 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.613 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.612 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.587 (+/-0.234) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.587 (+/-0.233) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'linear'}
0.666 (+/-0.315) for {'C': 10.0, 'kernel': 'linear'}
0.637 (+/-0.237) for {'C': 100.0, 'kernel': 'linear'}
0.637 (+/-0.239) for {'C': 1000.0, 'kernel': 'linear'}
0.585 (+/-0.236) for {'C': 10000.0, 'kernel': 'linear'}
0.611 (+/-0.242) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.20 0.17 0.18 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.99 0.99 629
本轮grid search结果,得到最好的参数选择是: {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.666025641025641
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.747 (+/-0.502) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.747 (+/-0.502) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.747 (+/-0.502) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.747 (+/-0.502) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.747 (+/-0.502) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.747 (+/-0.502) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.797 (+/-0.492) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.748 (+/-0.449) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.689 (+/-0.382) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.673 (+/-0.330) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.747 (+/-0.502) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.747 (+/-0.502) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.747 (+/-0.502) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.747 (+/-0.502) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.797 (+/-0.492) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.748 (+/-0.449) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.698 (+/-0.383) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.664 (+/-0.326) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.673 (+/-0.330) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.747 (+/-0.502) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.747 (+/-0.502) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.747 (+/-0.502) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.797 (+/-0.492) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.748 (+/-0.449) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.706 (+/-0.383) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.664 (+/-0.326) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.673 (+/-0.330) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.651 (+/-0.308) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.747 (+/-0.502) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.747 (+/-0.502) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.797 (+/-0.492) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.748 (+/-0.449) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.706 (+/-0.383) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.664 (+/-0.326) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.673 (+/-0.330) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.651 (+/-0.308) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.626 (+/-0.313) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.747 (+/-0.502) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.797 (+/-0.492) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.748 (+/-0.449) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.706 (+/-0.383) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.714 (+/-0.359) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.673 (+/-0.330) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.659 (+/-0.313) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.659 (+/-0.385) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.650 (+/-0.385) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.797 (+/-0.492) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.748 (+/-0.449) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.706 (+/-0.383) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.714 (+/-0.359) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.673 (+/-0.330) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.689 (+/-0.374) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.669 (+/-0.373) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.663 (+/-0.383) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.609 (+/-0.313) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.001}
0.797 (+/-0.492) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.748 (+/-0.449) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.706 (+/-0.383) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.714 (+/-0.359) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.673 (+/-0.330) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.689 (+/-0.374) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.669 (+/-0.373) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.663 (+/-0.383) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.647 (+/-0.387) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.618 (+/-0.322) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.1}
0.797 (+/-0.492) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.001}
0.748 (+/-0.449) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.706 (+/-0.383) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.714 (+/-0.359) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.673 (+/-0.330) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.689 (+/-0.374) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.669 (+/-0.373) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.663 (+/-0.383) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.658 (+/-0.387) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.622 (+/-0.319) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.614 (+/-0.327) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.1}
0.748 (+/-0.449) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.001}
0.706 (+/-0.383) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.714 (+/-0.359) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.673 (+/-0.330) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.689 (+/-0.374) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.669 (+/-0.373) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.663 (+/-0.383) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.658 (+/-0.388) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.630 (+/-0.328) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.618 (+/-0.322) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.614 (+/-0.327) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.1}
0.706 (+/-0.383) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.001}
0.714 (+/-0.359) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.673 (+/-0.330) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.689 (+/-0.374) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.669 (+/-0.373) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.663 (+/-0.383) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.658 (+/-0.388) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.660 (+/-0.385) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.622 (+/-0.319) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.614 (+/-0.327) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.614 (+/-0.327) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.1}
0.714 (+/-0.359) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.673 (+/-0.330) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.689 (+/-0.374) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.669 (+/-0.373) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.663 (+/-0.383) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.658 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.660 (+/-0.385) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.626 (+/-0.313) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.625 (+/-0.314) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.614 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.614 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 1.0, 'kernel': 'linear'}
0.674 (+/-0.375) for {'C': 10.0, 'kernel': 'linear'}
0.631 (+/-0.312) for {'C': 100.0, 'kernel': 'linear'}
0.637 (+/-0.307) for {'C': 1000.0, 'kernel': 'linear'}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'linear'}
0.620 (+/-0.321) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.617 (+/-0.238) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.617 (+/-0.238) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.617 (+/-0.238) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.617 (+/-0.238) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.617 (+/-0.238) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.617 (+/-0.238) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.642 (+/-0.236) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.666 (+/-0.316) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.666 (+/-0.315) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.666 (+/-0.315) for {'C': 9.999999999999998, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.617 (+/-0.238) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.617 (+/-0.238) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.617 (+/-0.238) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.617 (+/-0.238) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.642 (+/-0.236) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.666 (+/-0.316) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.666 (+/-0.315) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.666 (+/-0.315) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.665 (+/-0.315) for {'C': 15.848931924611135, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.617 (+/-0.238) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.617 (+/-0.238) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.617 (+/-0.238) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.642 (+/-0.236) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.666 (+/-0.316) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.666 (+/-0.315) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.666 (+/-0.315) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.665 (+/-0.315) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.665 (+/-0.314) for {'C': 25.118864315095795, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.617 (+/-0.238) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.617 (+/-0.238) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.642 (+/-0.236) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.666 (+/-0.316) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.666 (+/-0.315) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.666 (+/-0.315) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.665 (+/-0.315) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.665 (+/-0.314) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.639 (+/-0.325) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.617 (+/-0.238) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.642 (+/-0.236) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.666 (+/-0.316) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.666 (+/-0.315) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.682 (+/-0.295) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.665 (+/-0.315) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.665 (+/-0.314) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.639 (+/-0.325) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.636 (+/-0.327) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.642 (+/-0.236) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.666 (+/-0.316) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.666 (+/-0.315) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.682 (+/-0.295) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.665 (+/-0.315) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.665 (+/-0.315) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.664 (+/-0.315) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.661 (+/-0.315) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.608 (+/-0.244) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.001}
0.642 (+/-0.236) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.666 (+/-0.316) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.666 (+/-0.315) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.682 (+/-0.295) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.665 (+/-0.315) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.665 (+/-0.315) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.664 (+/-0.315) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.661 (+/-0.315) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.633 (+/-0.328) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.609 (+/-0.243) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.1}
0.642 (+/-0.236) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.316) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.666 (+/-0.315) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.682 (+/-0.295) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.665 (+/-0.315) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.665 (+/-0.315) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.664 (+/-0.315) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.661 (+/-0.315) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.658 (+/-0.314) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.634 (+/-0.326) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.585 (+/-0.237) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.1}
0.666 (+/-0.316) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.315) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.682 (+/-0.295) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.665 (+/-0.315) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.665 (+/-0.315) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.664 (+/-0.315) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.661 (+/-0.315) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.658 (+/-0.314) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.635 (+/-0.325) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.610 (+/-0.242) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.586 (+/-0.235) for {'C': 398.1071705534974, 'kernel': 'rbf', 'gamma': 0.1}
0.666 (+/-0.315) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.001}
0.682 (+/-0.295) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.665 (+/-0.315) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.665 (+/-0.315) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.664 (+/-0.315) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.661 (+/-0.315) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.658 (+/-0.314) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.660 (+/-0.314) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.635 (+/-0.324) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.586 (+/-0.236) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.586 (+/-0.235) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.1}
0.682 (+/-0.295) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.665 (+/-0.315) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.665 (+/-0.315) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.664 (+/-0.315) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.661 (+/-0.315) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.658 (+/-0.314) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.660 (+/-0.314) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.660 (+/-0.312) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.636 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.586 (+/-0.235) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.587 (+/-0.233) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.663 (+/-0.316) for {'C': 10.0, 'kernel': 'linear'}
0.661 (+/-0.315) for {'C': 100.0, 'kernel': 'linear'}
0.637 (+/-0.239) for {'C': 1000.0, 'kernel': 'linear'}
0.585 (+/-0.236) for {'C': 10000.0, 'kernel': 'linear'}
0.611 (+/-0.242) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.17 0.17 0.17 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6822115384615385
循环迭代之前,delta is: [3.69042656e+01 5.84893192e-03]
执行tunemodel()函数前,使用的grid search parameter是:
[{'C': array([ 39.81071706, 43.65158322, 47.86300923, 52.48074602,
57.54399373, 63.09573445, 69.18309709, 75.8577575 ,
83.17637711, 91.20108394, 100. ]), 'kernel': ['rbf'], 'gamma': array([0.01 , 0.01096478, 0.01202264, 0.01318257, 0.0144544 ,
0.01584893, 0.01737801, 0.01905461, 0.02089296, 0.02290868,
0.02511886])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.747 (+/-0.502) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.747 (+/-0.502) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.747 (+/-0.502) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.747 (+/-0.502) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.747 (+/-0.502) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.747 (+/-0.502) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.722 (+/-0.473) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.747 (+/-0.449) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.747 (+/-0.449) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.747 (+/-0.449) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.747 (+/-0.449) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.747 (+/-0.502) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.747 (+/-0.502) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.747 (+/-0.502) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.747 (+/-0.502) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.747 (+/-0.502) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.722 (+/-0.473) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.747 (+/-0.449) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.747 (+/-0.449) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.747 (+/-0.449) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.747 (+/-0.449) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.747 (+/-0.449) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.747 (+/-0.502) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.747 (+/-0.502) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.747 (+/-0.502) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.747 (+/-0.502) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.772 (+/-0.473) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.747 (+/-0.449) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.747 (+/-0.449) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.747 (+/-0.449) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.747 (+/-0.449) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.747 (+/-0.449) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.748 (+/-0.449) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.747 (+/-0.502) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.747 (+/-0.502) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.747 (+/-0.502) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.772 (+/-0.473) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.747 (+/-0.449) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.747 (+/-0.449) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.747 (+/-0.449) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.747 (+/-0.449) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.747 (+/-0.449) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.748 (+/-0.449) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.748 (+/-0.449) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.747 (+/-0.502) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.747 (+/-0.502) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.772 (+/-0.473) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.747 (+/-0.449) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.747 (+/-0.449) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.747 (+/-0.449) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.747 (+/-0.449) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.747 (+/-0.449) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.748 (+/-0.449) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.748 (+/-0.449) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.748 (+/-0.449) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.747 (+/-0.502) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.772 (+/-0.473) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.747 (+/-0.449) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.747 (+/-0.449) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.747 (+/-0.449) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.747 (+/-0.449) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.747 (+/-0.449) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.748 (+/-0.449) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.748 (+/-0.449) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.748 (+/-0.449) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.723 (+/-0.423) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.772 (+/-0.473) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.747 (+/-0.449) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.747 (+/-0.449) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.747 (+/-0.449) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.747 (+/-0.449) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.747 (+/-0.449) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.748 (+/-0.449) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.748 (+/-0.449) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.748 (+/-0.449) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.723 (+/-0.423) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.723 (+/-0.423) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.747 (+/-0.449) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.747 (+/-0.449) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.747 (+/-0.449) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.747 (+/-0.449) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.747 (+/-0.449) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.748 (+/-0.449) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.748 (+/-0.449) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.748 (+/-0.449) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.723 (+/-0.423) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.723 (+/-0.423) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.698 (+/-0.383) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.747 (+/-0.449) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.747 (+/-0.449) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.747 (+/-0.449) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.748 (+/-0.449) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.748 (+/-0.449) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.748 (+/-0.449) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.748 (+/-0.449) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.723 (+/-0.423) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.723 (+/-0.423) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.698 (+/-0.383) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.698 (+/-0.383) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.747 (+/-0.449) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.747 (+/-0.449) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.748 (+/-0.449) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.748 (+/-0.449) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.748 (+/-0.449) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.748 (+/-0.449) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.723 (+/-0.423) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.723 (+/-0.423) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.698 (+/-0.383) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.698 (+/-0.383) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.698 (+/-0.383) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.747 (+/-0.449) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.748 (+/-0.449) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.748 (+/-0.449) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.748 (+/-0.449) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.748 (+/-0.449) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.723 (+/-0.423) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.723 (+/-0.423) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.698 (+/-0.383) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.698 (+/-0.383) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.698 (+/-0.383) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.723 (+/-0.423) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'linear'}
0.706 (+/-0.383) for {'C': 10.0, 'kernel': 'linear'}
0.643 (+/-0.320) for {'C': 100.0, 'kernel': 'linear'}
0.637 (+/-0.307) for {'C': 1000.0, 'kernel': 'linear'}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'linear'}
0.620 (+/-0.321) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.617 (+/-0.238) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.617 (+/-0.238) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.617 (+/-0.238) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.617 (+/-0.238) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.617 (+/-0.238) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.617 (+/-0.238) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.617 (+/-0.238) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.641 (+/-0.236) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.641 (+/-0.236) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.641 (+/-0.236) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.641 (+/-0.236) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.617 (+/-0.238) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.617 (+/-0.238) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.617 (+/-0.238) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.617 (+/-0.238) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.617 (+/-0.238) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.617 (+/-0.238) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.641 (+/-0.236) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.641 (+/-0.236) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.641 (+/-0.236) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.641 (+/-0.236) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.641 (+/-0.236) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.617 (+/-0.238) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.617 (+/-0.238) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.617 (+/-0.238) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.617 (+/-0.238) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.642 (+/-0.236) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.641 (+/-0.236) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.641 (+/-0.236) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.641 (+/-0.236) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.641 (+/-0.236) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.641 (+/-0.236) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.666 (+/-0.316) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.617 (+/-0.238) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.617 (+/-0.238) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.617 (+/-0.238) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.642 (+/-0.236) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.641 (+/-0.236) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.641 (+/-0.236) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.641 (+/-0.236) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.641 (+/-0.236) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.641 (+/-0.236) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.666 (+/-0.316) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.666 (+/-0.316) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.617 (+/-0.238) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.617 (+/-0.238) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.642 (+/-0.236) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.641 (+/-0.236) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.641 (+/-0.236) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.641 (+/-0.236) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.641 (+/-0.236) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.641 (+/-0.236) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.666 (+/-0.316) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.666 (+/-0.316) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.666 (+/-0.316) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.617 (+/-0.238) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.642 (+/-0.236) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.641 (+/-0.236) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.641 (+/-0.236) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.641 (+/-0.236) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.641 (+/-0.236) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.641 (+/-0.236) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.666 (+/-0.316) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.666 (+/-0.316) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.666 (+/-0.316) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.666 (+/-0.315) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.642 (+/-0.236) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.641 (+/-0.236) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.641 (+/-0.236) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.641 (+/-0.236) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.641 (+/-0.236) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.641 (+/-0.236) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.666 (+/-0.316) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.666 (+/-0.316) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.666 (+/-0.316) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.666 (+/-0.315) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.666 (+/-0.315) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.641 (+/-0.236) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.641 (+/-0.236) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.641 (+/-0.236) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.641 (+/-0.236) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.641 (+/-0.236) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.666 (+/-0.316) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.666 (+/-0.316) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.666 (+/-0.316) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.666 (+/-0.315) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.666 (+/-0.315) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.666 (+/-0.315) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.641 (+/-0.236) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.641 (+/-0.236) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.641 (+/-0.236) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.666 (+/-0.316) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.666 (+/-0.316) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.666 (+/-0.316) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.666 (+/-0.316) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.666 (+/-0.315) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.666 (+/-0.315) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.666 (+/-0.315) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.666 (+/-0.315) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.641 (+/-0.236) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.641 (+/-0.236) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.666 (+/-0.316) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.666 (+/-0.316) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.666 (+/-0.316) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.666 (+/-0.316) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.666 (+/-0.315) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.666 (+/-0.315) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.666 (+/-0.315) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.666 (+/-0.315) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.666 (+/-0.315) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.641 (+/-0.236) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.666 (+/-0.316) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.666 (+/-0.316) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.666 (+/-0.316) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.666 (+/-0.316) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.666 (+/-0.315) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.666 (+/-0.315) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.666 (+/-0.315) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.666 (+/-0.315) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.666 (+/-0.315) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.666 (+/-0.315) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'linear'}
0.666 (+/-0.315) for {'C': 10.0, 'kernel': 'linear'}
0.637 (+/-0.237) for {'C': 100.0, 'kernel': 'linear'}
0.637 (+/-0.239) for {'C': 1000.0, 'kernel': 'linear'}
0.585 (+/-0.236) for {'C': 10000.0, 'kernel': 'linear'}
0.611 (+/-0.242) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.20 0.17 0.18 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.99 0.99 629
本轮grid search结果,得到最好的参数选择是: {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6663461538461538
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.748 (+/-0.449) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.748 (+/-0.449) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.748 (+/-0.449) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.748 (+/-0.449) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.748 (+/-0.449) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.706 (+/-0.383) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.685 (+/-0.384) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.677 (+/-0.374) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.727 (+/-0.397) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.735 (+/-0.402) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.664 (+/-0.326) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.748 (+/-0.449) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.748 (+/-0.449) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.748 (+/-0.449) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.748 (+/-0.449) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.706 (+/-0.383) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.685 (+/-0.384) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.677 (+/-0.374) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.727 (+/-0.397) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.735 (+/-0.402) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.664 (+/-0.326) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.664 (+/-0.326) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.748 (+/-0.449) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.748 (+/-0.449) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.748 (+/-0.449) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.706 (+/-0.383) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.685 (+/-0.384) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.677 (+/-0.374) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.727 (+/-0.397) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.735 (+/-0.402) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.664 (+/-0.326) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.664 (+/-0.326) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.673 (+/-0.330) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.748 (+/-0.449) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.748 (+/-0.449) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.706 (+/-0.383) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.685 (+/-0.384) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.677 (+/-0.374) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.727 (+/-0.397) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.710 (+/-0.363) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.714 (+/-0.359) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.664 (+/-0.326) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.673 (+/-0.330) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.673 (+/-0.330) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.748 (+/-0.449) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.706 (+/-0.383) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.685 (+/-0.384) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.677 (+/-0.374) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.727 (+/-0.397) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.710 (+/-0.363) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.714 (+/-0.359) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.664 (+/-0.326) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.673 (+/-0.330) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.673 (+/-0.330) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.673 (+/-0.330) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.706 (+/-0.383) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.685 (+/-0.384) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.677 (+/-0.374) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.727 (+/-0.397) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.710 (+/-0.363) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.714 (+/-0.359) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.714 (+/-0.359) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.673 (+/-0.330) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.673 (+/-0.330) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.673 (+/-0.330) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.673 (+/-0.330) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.685 (+/-0.384) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.677 (+/-0.374) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.727 (+/-0.397) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.710 (+/-0.363) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.714 (+/-0.359) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.714 (+/-0.359) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.673 (+/-0.330) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.673 (+/-0.330) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.673 (+/-0.330) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.673 (+/-0.330) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.673 (+/-0.330) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.677 (+/-0.374) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.727 (+/-0.397) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.710 (+/-0.363) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.714 (+/-0.359) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.714 (+/-0.359) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.673 (+/-0.330) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.673 (+/-0.330) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.673 (+/-0.330) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.673 (+/-0.330) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.673 (+/-0.330) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.673 (+/-0.330) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.727 (+/-0.397) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.710 (+/-0.363) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.714 (+/-0.359) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.714 (+/-0.359) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.673 (+/-0.330) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.673 (+/-0.330) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.673 (+/-0.330) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.673 (+/-0.330) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.673 (+/-0.330) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.673 (+/-0.330) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.664 (+/-0.317) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.710 (+/-0.363) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.714 (+/-0.359) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.714 (+/-0.359) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.673 (+/-0.330) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.673 (+/-0.330) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.673 (+/-0.330) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.673 (+/-0.330) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.673 (+/-0.330) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.673 (+/-0.330) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.664 (+/-0.317) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.664 (+/-0.317) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.714 (+/-0.359) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.714 (+/-0.359) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.673 (+/-0.330) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.673 (+/-0.330) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.673 (+/-0.330) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.673 (+/-0.330) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.673 (+/-0.330) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.673 (+/-0.330) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.664 (+/-0.317) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.664 (+/-0.317) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.689 (+/-0.374) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 1.0, 'kernel': 'linear'}
0.674 (+/-0.375) for {'C': 10.0, 'kernel': 'linear'}
0.631 (+/-0.312) for {'C': 100.0, 'kernel': 'linear'}
0.637 (+/-0.307) for {'C': 1000.0, 'kernel': 'linear'}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'linear'}
0.620 (+/-0.321) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.20 0.17 0.18 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.666 (+/-0.316) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.666 (+/-0.316) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.666 (+/-0.316) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.666 (+/-0.316) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.666 (+/-0.316) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.666 (+/-0.315) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.666 (+/-0.315) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.665 (+/-0.314) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.682 (+/-0.294) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.682 (+/-0.295) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.666 (+/-0.315) for {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.666 (+/-0.316) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.666 (+/-0.316) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.666 (+/-0.316) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.666 (+/-0.316) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.666 (+/-0.315) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.666 (+/-0.315) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.665 (+/-0.314) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.682 (+/-0.294) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.682 (+/-0.295) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.666 (+/-0.315) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.666 (+/-0.315) for {'C': 43.651583224016576, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.666 (+/-0.316) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.666 (+/-0.316) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.666 (+/-0.316) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.666 (+/-0.315) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.666 (+/-0.315) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.665 (+/-0.314) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.682 (+/-0.294) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.682 (+/-0.295) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.666 (+/-0.315) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.666 (+/-0.315) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.666 (+/-0.315) for {'C': 47.863009232263806, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.666 (+/-0.316) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.666 (+/-0.316) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.666 (+/-0.315) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.666 (+/-0.315) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.665 (+/-0.314) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.682 (+/-0.294) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.682 (+/-0.295) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.682 (+/-0.295) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.666 (+/-0.315) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.666 (+/-0.315) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.666 (+/-0.315) for {'C': 52.480746024977265, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.666 (+/-0.316) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.666 (+/-0.315) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.666 (+/-0.315) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.665 (+/-0.314) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.682 (+/-0.294) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.682 (+/-0.295) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.682 (+/-0.295) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.666 (+/-0.315) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.666 (+/-0.315) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.666 (+/-0.315) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.666 (+/-0.315) for {'C': 57.543993733715695, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.666 (+/-0.315) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.666 (+/-0.315) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.665 (+/-0.314) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.682 (+/-0.294) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.682 (+/-0.295) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.682 (+/-0.295) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.682 (+/-0.295) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.666 (+/-0.315) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.666 (+/-0.315) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.666 (+/-0.315) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.665 (+/-0.315) for {'C': 63.09573444801932, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.666 (+/-0.315) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.665 (+/-0.314) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.682 (+/-0.294) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.682 (+/-0.295) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.682 (+/-0.295) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.682 (+/-0.295) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.666 (+/-0.315) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.666 (+/-0.315) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.666 (+/-0.315) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.665 (+/-0.315) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.665 (+/-0.315) for {'C': 69.18309709189364, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.665 (+/-0.314) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.682 (+/-0.294) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.682 (+/-0.295) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.682 (+/-0.295) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.682 (+/-0.295) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.666 (+/-0.315) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.666 (+/-0.315) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.666 (+/-0.315) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.665 (+/-0.315) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.665 (+/-0.315) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.665 (+/-0.315) for {'C': 75.85775750291837, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.682 (+/-0.294) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.682 (+/-0.295) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.682 (+/-0.295) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.682 (+/-0.295) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.666 (+/-0.315) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.666 (+/-0.315) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.666 (+/-0.315) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.665 (+/-0.315) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.665 (+/-0.315) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.665 (+/-0.315) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.665 (+/-0.315) for {'C': 83.17637711026714, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.682 (+/-0.295) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.682 (+/-0.295) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.682 (+/-0.295) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.666 (+/-0.315) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.666 (+/-0.315) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.666 (+/-0.315) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.665 (+/-0.315) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.665 (+/-0.315) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.665 (+/-0.315) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.665 (+/-0.315) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.665 (+/-0.315) for {'C': 91.20108393559102, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.682 (+/-0.295) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.682 (+/-0.295) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.010964781961431852}
0.666 (+/-0.315) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.01202264434617413}
0.666 (+/-0.315) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.013182567385564073}
0.666 (+/-0.315) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.014454397707459276}
0.665 (+/-0.315) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.665 (+/-0.315) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.017378008287493765}
0.665 (+/-0.315) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.019054607179632484}
0.665 (+/-0.315) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.665 (+/-0.315) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.665 (+/-0.315) for {'C': 100.00000000000004, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.663 (+/-0.316) for {'C': 10.0, 'kernel': 'linear'}
0.661 (+/-0.315) for {'C': 100.0, 'kernel': 'linear'}
0.637 (+/-0.239) for {'C': 1000.0, 'kernel': 'linear'}
0.585 (+/-0.236) for {'C': 10000.0, 'kernel': 'linear'}
0.611 (+/-0.242) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.17 0.17 0.17 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6822115384615385
发现最优参数C为原先的最大/最小值,直接重新设置超参。
第0轮loop中,tunemodel函数得到的最优参数是: ([{'C': array([3.98107171e-04, 3.98107171e-03, 3.98107171e-02, 3.98107171e-01,
3.98107171e+00, 3.98107171e+01, 3.98107171e+02, 3.98107171e+03,
3.98107171e+04, 3.98107171e+05, 3.98107171e+06]), 'kernel': ['rbf'], 'gamma': array([0.02089296, 0.02128139, 0.02167704, 0.02208005, 0.02249055,
0.02290868, 0.02333458, 0.0237684 , 0.02421029, 0.02466039,
0.02511886])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}], {'C': 39.810717055349706, 'kernel': 'rbf', 'gamma': 0.02290867652767774}, 0.6822115384615385)
这是第0次迭代微调C和gamma。
第0次迭代,得到delta: [2.32850174e+01 7.05974460e-03]
执行tunemodel()函数前,使用的grid search parameter是:
[{'C': array([3.98107171e-04, 3.98107171e-03, 3.98107171e-02, 3.98107171e-01,
3.98107171e+00, 3.98107171e+01, 3.98107171e+02, 3.98107171e+03,
3.98107171e+04, 3.98107171e+05, 3.98107171e+06]), 'kernel': ['rbf'], 'gamma': array([0.02089296, 0.02128139, 0.02167704, 0.02208005, 0.02249055,
0.02290868, 0.02333458, 0.0237684 , 0.02421029, 0.02466039,
0.02511886])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.496 (+/-0.001) for {'C': 0.0003981071705534969, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.496 (+/-0.001) for {'C': 0.0003981071705534969, 'kernel': 'rbf', 'gamma': 0.021281390459827135}
0.496 (+/-0.001) for {'C': 0.0003981071705534969, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.496 (+/-0.001) for {'C': 0.0003981071705534969, 'kernel': 'rbf', 'gamma': 0.022080047330189013}
0.496 (+/-0.001) for {'C': 0.0003981071705534969, 'kernel': 'rbf', 'gamma': 0.022490546058357815}
0.496 (+/-0.001) for {'C': 0.0003981071705534969, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.496 (+/-0.001) for {'C': 0.0003981071705534969, 'kernel': 'rbf', 'gamma': 0.023334580622810044}
0.496 (+/-0.001) for {'C': 0.0003981071705534969, 'kernel': 'rbf', 'gamma': 0.023768402866248775}
0.496 (+/-0.001) for {'C': 0.0003981071705534969, 'kernel': 'rbf', 'gamma': 0.024210290467361794}
0.496 (+/-0.001) for {'C': 0.0003981071705534969, 'kernel': 'rbf', 'gamma': 0.0246603933723434}
0.496 (+/-0.001) for {'C': 0.0003981071705534969, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.496 (+/-0.001) for {'C': 0.003981071705534969, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.496 (+/-0.001) for {'C': 0.003981071705534969, 'kernel': 'rbf', 'gamma': 0.021281390459827135}
0.496 (+/-0.001) for {'C': 0.003981071705534969, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.496 (+/-0.001) for {'C': 0.003981071705534969, 'kernel': 'rbf', 'gamma': 0.022080047330189013}
0.496 (+/-0.001) for {'C': 0.003981071705534969, 'kernel': 'rbf', 'gamma': 0.022490546058357815}
0.496 (+/-0.001) for {'C': 0.003981071705534969, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.496 (+/-0.001) for {'C': 0.003981071705534969, 'kernel': 'rbf', 'gamma': 0.023334580622810044}
0.496 (+/-0.001) for {'C': 0.003981071705534969, 'kernel': 'rbf', 'gamma': 0.023768402866248775}
0.496 (+/-0.001) for {'C': 0.003981071705534969, 'kernel': 'rbf', 'gamma': 0.024210290467361794}
0.496 (+/-0.001) for {'C': 0.003981071705534969, 'kernel': 'rbf', 'gamma': 0.0246603933723434}
0.496 (+/-0.001) for {'C': 0.003981071705534969, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.496 (+/-0.001) for {'C': 0.03981071705534969, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.496 (+/-0.001) for {'C': 0.03981071705534969, 'kernel': 'rbf', 'gamma': 0.021281390459827135}
0.496 (+/-0.001) for {'C': 0.03981071705534969, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.496 (+/-0.001) for {'C': 0.03981071705534969, 'kernel': 'rbf', 'gamma': 0.022080047330189013}
0.496 (+/-0.001) for {'C': 0.03981071705534969, 'kernel': 'rbf', 'gamma': 0.022490546058357815}
0.496 (+/-0.001) for {'C': 0.03981071705534969, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.496 (+/-0.001) for {'C': 0.03981071705534969, 'kernel': 'rbf', 'gamma': 0.023334580622810044}
0.496 (+/-0.001) for {'C': 0.03981071705534969, 'kernel': 'rbf', 'gamma': 0.023768402866248775}
0.496 (+/-0.001) for {'C': 0.03981071705534969, 'kernel': 'rbf', 'gamma': 0.024210290467361794}
0.496 (+/-0.001) for {'C': 0.03981071705534969, 'kernel': 'rbf', 'gamma': 0.0246603933723434}
0.496 (+/-0.001) for {'C': 0.03981071705534969, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.496 (+/-0.001) for {'C': 0.3981071705534969, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.496 (+/-0.001) for {'C': 0.3981071705534969, 'kernel': 'rbf', 'gamma': 0.021281390459827135}
0.496 (+/-0.001) for {'C': 0.3981071705534969, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.496 (+/-0.001) for {'C': 0.3981071705534969, 'kernel': 'rbf', 'gamma': 0.022080047330189013}
0.496 (+/-0.001) for {'C': 0.3981071705534969, 'kernel': 'rbf', 'gamma': 0.022490546058357815}
0.496 (+/-0.001) for {'C': 0.3981071705534969, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.496 (+/-0.001) for {'C': 0.3981071705534969, 'kernel': 'rbf', 'gamma': 0.023334580622810044}
0.496 (+/-0.001) for {'C': 0.3981071705534969, 'kernel': 'rbf', 'gamma': 0.023768402866248775}
0.496 (+/-0.001) for {'C': 0.3981071705534969, 'kernel': 'rbf', 'gamma': 0.024210290467361794}
0.496 (+/-0.001) for {'C': 0.3981071705534969, 'kernel': 'rbf', 'gamma': 0.0246603933723434}
0.496 (+/-0.001) for {'C': 0.3981071705534969, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.747 (+/-0.502) for {'C': 3.981071705534969, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.747 (+/-0.502) for {'C': 3.981071705534969, 'kernel': 'rbf', 'gamma': 0.021281390459827135}
0.747 (+/-0.502) for {'C': 3.981071705534969, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.747 (+/-0.502) for {'C': 3.981071705534969, 'kernel': 'rbf', 'gamma': 0.022080047330189013}
0.747 (+/-0.502) for {'C': 3.981071705534969, 'kernel': 'rbf', 'gamma': 0.022490546058357815}
0.747 (+/-0.502) for {'C': 3.981071705534969, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.747 (+/-0.502) for {'C': 3.981071705534969, 'kernel': 'rbf', 'gamma': 0.023334580622810044}
0.747 (+/-0.502) for {'C': 3.981071705534969, 'kernel': 'rbf', 'gamma': 0.023768402866248775}
0.747 (+/-0.502) for {'C': 3.981071705534969, 'kernel': 'rbf', 'gamma': 0.024210290467361794}
0.747 (+/-0.502) for {'C': 3.981071705534969, 'kernel': 'rbf', 'gamma': 0.0246603933723434}
0.747 (+/-0.502) for {'C': 3.981071705534969, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.747 (+/-0.449) for {'C': 39.81071705534969, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.747 (+/-0.449) for {'C': 39.81071705534969, 'kernel': 'rbf', 'gamma': 0.021281390459827135}
0.747 (+/-0.449) for {'C': 39.81071705534969, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.747 (+/-0.449) for {'C': 39.81071705534969, 'kernel': 'rbf', 'gamma': 0.022080047330189013}
0.747 (+/-0.449) for {'C': 39.81071705534969, 'kernel': 'rbf', 'gamma': 0.022490546058357815}
0.747 (+/-0.449) for {'C': 39.81071705534969, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.747 (+/-0.449) for {'C': 39.81071705534969, 'kernel': 'rbf', 'gamma': 0.023334580622810044}
0.747 (+/-0.449) for {'C': 39.81071705534969, 'kernel': 'rbf', 'gamma': 0.023768402866248775}
0.747 (+/-0.449) for {'C': 39.81071705534969, 'kernel': 'rbf', 'gamma': 0.024210290467361794}
0.747 (+/-0.449) for {'C': 39.81071705534969, 'kernel': 'rbf', 'gamma': 0.0246603933723434}
0.747 (+/-0.449) for {'C': 39.81071705534969, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.722 (+/-0.417) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.722 (+/-0.417) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.021281390459827135}
0.722 (+/-0.417) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.672 (+/-0.392) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.022080047330189013}
0.672 (+/-0.392) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.022490546058357815}
0.672 (+/-0.392) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.672 (+/-0.392) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.023334580622810044}
0.672 (+/-0.392) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.023768402866248775}
0.672 (+/-0.392) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.024210290467361794}
0.672 (+/-0.392) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.0246603933723434}
0.672 (+/-0.392) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.632 (+/-0.311) for {'C': 3981.071705534969, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.629 (+/-0.312) for {'C': 3981.071705534969, 'kernel': 'rbf', 'gamma': 0.021281390459827135}
0.629 (+/-0.312) for {'C': 3981.071705534969, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.629 (+/-0.312) for {'C': 3981.071705534969, 'kernel': 'rbf', 'gamma': 0.022080047330189013}
0.629 (+/-0.312) for {'C': 3981.071705534969, 'kernel': 'rbf', 'gamma': 0.022490546058357815}
0.630 (+/-0.311) for {'C': 3981.071705534969, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.630 (+/-0.311) for {'C': 3981.071705534969, 'kernel': 'rbf', 'gamma': 0.023334580622810044}
0.630 (+/-0.311) for {'C': 3981.071705534969, 'kernel': 'rbf', 'gamma': 0.023768402866248775}
0.619 (+/-0.321) for {'C': 3981.071705534969, 'kernel': 'rbf', 'gamma': 0.024210290467361794}
0.619 (+/-0.321) for {'C': 3981.071705534969, 'kernel': 'rbf', 'gamma': 0.0246603933723434}
0.619 (+/-0.321) for {'C': 3981.071705534969, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.614 (+/-0.327) for {'C': 39810.71705534969, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.614 (+/-0.327) for {'C': 39810.71705534969, 'kernel': 'rbf', 'gamma': 0.021281390459827135}
0.614 (+/-0.327) for {'C': 39810.71705534969, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.614 (+/-0.327) for {'C': 39810.71705534969, 'kernel': 'rbf', 'gamma': 0.022080047330189013}
0.614 (+/-0.327) for {'C': 39810.71705534969, 'kernel': 'rbf', 'gamma': 0.022490546058357815}
0.614 (+/-0.327) for {'C': 39810.71705534969, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.614 (+/-0.327) for {'C': 39810.71705534969, 'kernel': 'rbf', 'gamma': 0.023334580622810044}
0.614 (+/-0.327) for {'C': 39810.71705534969, 'kernel': 'rbf', 'gamma': 0.023768402866248775}
0.614 (+/-0.327) for {'C': 39810.71705534969, 'kernel': 'rbf', 'gamma': 0.024210290467361794}
0.614 (+/-0.327) for {'C': 39810.71705534969, 'kernel': 'rbf', 'gamma': 0.0246603933723434}
0.614 (+/-0.327) for {'C': 39810.71705534969, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.614 (+/-0.327) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.614 (+/-0.327) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 0.021281390459827135}
0.614 (+/-0.327) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.614 (+/-0.327) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 0.022080047330189013}
0.614 (+/-0.327) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 0.022490546058357815}
0.614 (+/-0.327) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.614 (+/-0.327) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 0.023334580622810044}
0.614 (+/-0.327) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 0.023768402866248775}
0.614 (+/-0.327) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 0.024210290467361794}
0.614 (+/-0.327) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 0.0246603933723434}
0.614 (+/-0.327) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.614 (+/-0.327) for {'C': 3981071.7055349695, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.614 (+/-0.327) for {'C': 3981071.7055349695, 'kernel': 'rbf', 'gamma': 0.021281390459827135}
0.614 (+/-0.327) for {'C': 3981071.7055349695, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.614 (+/-0.327) for {'C': 3981071.7055349695, 'kernel': 'rbf', 'gamma': 0.022080047330189013}
0.614 (+/-0.327) for {'C': 3981071.7055349695, 'kernel': 'rbf', 'gamma': 0.022490546058357815}
0.614 (+/-0.327) for {'C': 3981071.7055349695, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.614 (+/-0.327) for {'C': 3981071.7055349695, 'kernel': 'rbf', 'gamma': 0.023334580622810044}
0.614 (+/-0.327) for {'C': 3981071.7055349695, 'kernel': 'rbf', 'gamma': 0.023768402866248775}
0.614 (+/-0.327) for {'C': 3981071.7055349695, 'kernel': 'rbf', 'gamma': 0.024210290467361794}
0.614 (+/-0.327) for {'C': 3981071.7055349695, 'kernel': 'rbf', 'gamma': 0.0246603933723434}
0.614 (+/-0.327) for {'C': 3981071.7055349695, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'linear'}
0.706 (+/-0.383) for {'C': 10.0, 'kernel': 'linear'}
0.643 (+/-0.320) for {'C': 100.0, 'kernel': 'linear'}
0.637 (+/-0.307) for {'C': 1000.0, 'kernel': 'linear'}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'linear'}
0.620 (+/-0.321) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.25 0.17 0.20 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.500 (+/-0.000) for {'C': 0.0003981071705534969, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.500 (+/-0.000) for {'C': 0.0003981071705534969, 'kernel': 'rbf', 'gamma': 0.021281390459827135}
0.500 (+/-0.000) for {'C': 0.0003981071705534969, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.500 (+/-0.000) for {'C': 0.0003981071705534969, 'kernel': 'rbf', 'gamma': 0.022080047330189013}
0.500 (+/-0.000) for {'C': 0.0003981071705534969, 'kernel': 'rbf', 'gamma': 0.022490546058357815}
0.500 (+/-0.000) for {'C': 0.0003981071705534969, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.500 (+/-0.000) for {'C': 0.0003981071705534969, 'kernel': 'rbf', 'gamma': 0.023334580622810044}
0.500 (+/-0.000) for {'C': 0.0003981071705534969, 'kernel': 'rbf', 'gamma': 0.023768402866248775}
0.500 (+/-0.000) for {'C': 0.0003981071705534969, 'kernel': 'rbf', 'gamma': 0.024210290467361794}
0.500 (+/-0.000) for {'C': 0.0003981071705534969, 'kernel': 'rbf', 'gamma': 0.0246603933723434}
0.500 (+/-0.000) for {'C': 0.0003981071705534969, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.500 (+/-0.000) for {'C': 0.003981071705534969, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.500 (+/-0.000) for {'C': 0.003981071705534969, 'kernel': 'rbf', 'gamma': 0.021281390459827135}
0.500 (+/-0.000) for {'C': 0.003981071705534969, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.500 (+/-0.000) for {'C': 0.003981071705534969, 'kernel': 'rbf', 'gamma': 0.022080047330189013}
0.500 (+/-0.000) for {'C': 0.003981071705534969, 'kernel': 'rbf', 'gamma': 0.022490546058357815}
0.500 (+/-0.000) for {'C': 0.003981071705534969, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.500 (+/-0.000) for {'C': 0.003981071705534969, 'kernel': 'rbf', 'gamma': 0.023334580622810044}
0.500 (+/-0.000) for {'C': 0.003981071705534969, 'kernel': 'rbf', 'gamma': 0.023768402866248775}
0.500 (+/-0.000) for {'C': 0.003981071705534969, 'kernel': 'rbf', 'gamma': 0.024210290467361794}
0.500 (+/-0.000) for {'C': 0.003981071705534969, 'kernel': 'rbf', 'gamma': 0.0246603933723434}
0.500 (+/-0.000) for {'C': 0.003981071705534969, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.500 (+/-0.000) for {'C': 0.03981071705534969, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.500 (+/-0.000) for {'C': 0.03981071705534969, 'kernel': 'rbf', 'gamma': 0.021281390459827135}
0.500 (+/-0.000) for {'C': 0.03981071705534969, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.500 (+/-0.000) for {'C': 0.03981071705534969, 'kernel': 'rbf', 'gamma': 0.022080047330189013}
0.500 (+/-0.000) for {'C': 0.03981071705534969, 'kernel': 'rbf', 'gamma': 0.022490546058357815}
0.500 (+/-0.000) for {'C': 0.03981071705534969, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.500 (+/-0.000) for {'C': 0.03981071705534969, 'kernel': 'rbf', 'gamma': 0.023334580622810044}
0.500 (+/-0.000) for {'C': 0.03981071705534969, 'kernel': 'rbf', 'gamma': 0.023768402866248775}
0.500 (+/-0.000) for {'C': 0.03981071705534969, 'kernel': 'rbf', 'gamma': 0.024210290467361794}
0.500 (+/-0.000) for {'C': 0.03981071705534969, 'kernel': 'rbf', 'gamma': 0.0246603933723434}
0.500 (+/-0.000) for {'C': 0.03981071705534969, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.500 (+/-0.000) for {'C': 0.3981071705534969, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.500 (+/-0.000) for {'C': 0.3981071705534969, 'kernel': 'rbf', 'gamma': 0.021281390459827135}
0.500 (+/-0.000) for {'C': 0.3981071705534969, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.500 (+/-0.000) for {'C': 0.3981071705534969, 'kernel': 'rbf', 'gamma': 0.022080047330189013}
0.500 (+/-0.000) for {'C': 0.3981071705534969, 'kernel': 'rbf', 'gamma': 0.022490546058357815}
0.500 (+/-0.000) for {'C': 0.3981071705534969, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.500 (+/-0.000) for {'C': 0.3981071705534969, 'kernel': 'rbf', 'gamma': 0.023334580622810044}
0.500 (+/-0.000) for {'C': 0.3981071705534969, 'kernel': 'rbf', 'gamma': 0.023768402866248775}
0.500 (+/-0.000) for {'C': 0.3981071705534969, 'kernel': 'rbf', 'gamma': 0.024210290467361794}
0.500 (+/-0.000) for {'C': 0.3981071705534969, 'kernel': 'rbf', 'gamma': 0.0246603933723434}
0.500 (+/-0.000) for {'C': 0.3981071705534969, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.617 (+/-0.238) for {'C': 3.981071705534969, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.617 (+/-0.238) for {'C': 3.981071705534969, 'kernel': 'rbf', 'gamma': 0.021281390459827135}
0.617 (+/-0.238) for {'C': 3.981071705534969, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.617 (+/-0.238) for {'C': 3.981071705534969, 'kernel': 'rbf', 'gamma': 0.022080047330189013}
0.617 (+/-0.238) for {'C': 3.981071705534969, 'kernel': 'rbf', 'gamma': 0.022490546058357815}
0.617 (+/-0.238) for {'C': 3.981071705534969, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.617 (+/-0.238) for {'C': 3.981071705534969, 'kernel': 'rbf', 'gamma': 0.023334580622810044}
0.617 (+/-0.238) for {'C': 3.981071705534969, 'kernel': 'rbf', 'gamma': 0.023768402866248775}
0.617 (+/-0.238) for {'C': 3.981071705534969, 'kernel': 'rbf', 'gamma': 0.024210290467361794}
0.617 (+/-0.238) for {'C': 3.981071705534969, 'kernel': 'rbf', 'gamma': 0.0246603933723434}
0.617 (+/-0.238) for {'C': 3.981071705534969, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.641 (+/-0.236) for {'C': 39.81071705534969, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.641 (+/-0.236) for {'C': 39.81071705534969, 'kernel': 'rbf', 'gamma': 0.021281390459827135}
0.641 (+/-0.236) for {'C': 39.81071705534969, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.641 (+/-0.236) for {'C': 39.81071705534969, 'kernel': 'rbf', 'gamma': 0.022080047330189013}
0.641 (+/-0.236) for {'C': 39.81071705534969, 'kernel': 'rbf', 'gamma': 0.022490546058357815}
0.641 (+/-0.236) for {'C': 39.81071705534969, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.641 (+/-0.236) for {'C': 39.81071705534969, 'kernel': 'rbf', 'gamma': 0.023334580622810044}
0.641 (+/-0.236) for {'C': 39.81071705534969, 'kernel': 'rbf', 'gamma': 0.023768402866248775}
0.641 (+/-0.236) for {'C': 39.81071705534969, 'kernel': 'rbf', 'gamma': 0.024210290467361794}
0.641 (+/-0.236) for {'C': 39.81071705534969, 'kernel': 'rbf', 'gamma': 0.0246603933723434}
0.641 (+/-0.236) for {'C': 39.81071705534969, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.641 (+/-0.237) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.641 (+/-0.237) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.021281390459827135}
0.641 (+/-0.237) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.616 (+/-0.238) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.022080047330189013}
0.616 (+/-0.238) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.022490546058357815}
0.616 (+/-0.238) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.616 (+/-0.238) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.023334580622810044}
0.616 (+/-0.238) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.023768402866248775}
0.616 (+/-0.238) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.024210290467361794}
0.616 (+/-0.238) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.0246603933723434}
0.616 (+/-0.238) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.637 (+/-0.238) for {'C': 3981.071705534969, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.637 (+/-0.237) for {'C': 3981.071705534969, 'kernel': 'rbf', 'gamma': 0.021281390459827135}
0.637 (+/-0.237) for {'C': 3981.071705534969, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.637 (+/-0.237) for {'C': 3981.071705534969, 'kernel': 'rbf', 'gamma': 0.022080047330189013}
0.637 (+/-0.237) for {'C': 3981.071705534969, 'kernel': 'rbf', 'gamma': 0.022490546058357815}
0.637 (+/-0.238) for {'C': 3981.071705534969, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.637 (+/-0.238) for {'C': 3981.071705534969, 'kernel': 'rbf', 'gamma': 0.023334580622810044}
0.637 (+/-0.238) for {'C': 3981.071705534969, 'kernel': 'rbf', 'gamma': 0.023768402866248775}
0.612 (+/-0.240) for {'C': 3981.071705534969, 'kernel': 'rbf', 'gamma': 0.024210290467361794}
0.612 (+/-0.240) for {'C': 3981.071705534969, 'kernel': 'rbf', 'gamma': 0.0246603933723434}
0.612 (+/-0.240) for {'C': 3981.071705534969, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.588 (+/-0.232) for {'C': 39810.71705534969, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.588 (+/-0.232) for {'C': 39810.71705534969, 'kernel': 'rbf', 'gamma': 0.021281390459827135}
0.588 (+/-0.232) for {'C': 39810.71705534969, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.588 (+/-0.232) for {'C': 39810.71705534969, 'kernel': 'rbf', 'gamma': 0.022080047330189013}
0.588 (+/-0.232) for {'C': 39810.71705534969, 'kernel': 'rbf', 'gamma': 0.022490546058357815}
0.588 (+/-0.233) for {'C': 39810.71705534969, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.588 (+/-0.233) for {'C': 39810.71705534969, 'kernel': 'rbf', 'gamma': 0.023334580622810044}
0.588 (+/-0.233) for {'C': 39810.71705534969, 'kernel': 'rbf', 'gamma': 0.023768402866248775}
0.587 (+/-0.233) for {'C': 39810.71705534969, 'kernel': 'rbf', 'gamma': 0.024210290467361794}
0.587 (+/-0.233) for {'C': 39810.71705534969, 'kernel': 'rbf', 'gamma': 0.0246603933723434}
0.587 (+/-0.234) for {'C': 39810.71705534969, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.587 (+/-0.235) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.587 (+/-0.235) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 0.021281390459827135}
0.587 (+/-0.235) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.587 (+/-0.235) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 0.022080047330189013}
0.587 (+/-0.235) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 0.022490546058357815}
0.587 (+/-0.235) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.587 (+/-0.235) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 0.023334580622810044}
0.587 (+/-0.235) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 0.023768402866248775}
0.586 (+/-0.235) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 0.024210290467361794}
0.587 (+/-0.235) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 0.0246603933723434}
0.587 (+/-0.235) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.587 (+/-0.235) for {'C': 3981071.7055349695, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.587 (+/-0.235) for {'C': 3981071.7055349695, 'kernel': 'rbf', 'gamma': 0.021281390459827135}
0.587 (+/-0.235) for {'C': 3981071.7055349695, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.587 (+/-0.235) for {'C': 3981071.7055349695, 'kernel': 'rbf', 'gamma': 0.022080047330189013}
0.587 (+/-0.235) for {'C': 3981071.7055349695, 'kernel': 'rbf', 'gamma': 0.022490546058357815}
0.587 (+/-0.235) for {'C': 3981071.7055349695, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.587 (+/-0.235) for {'C': 3981071.7055349695, 'kernel': 'rbf', 'gamma': 0.023334580622810044}
0.587 (+/-0.235) for {'C': 3981071.7055349695, 'kernel': 'rbf', 'gamma': 0.023768402866248775}
0.586 (+/-0.235) for {'C': 3981071.7055349695, 'kernel': 'rbf', 'gamma': 0.024210290467361794}
0.587 (+/-0.235) for {'C': 3981071.7055349695, 'kernel': 'rbf', 'gamma': 0.0246603933723434}
0.587 (+/-0.235) for {'C': 3981071.7055349695, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'linear'}
0.666 (+/-0.315) for {'C': 10.0, 'kernel': 'linear'}
0.637 (+/-0.237) for {'C': 100.0, 'kernel': 'linear'}
0.637 (+/-0.239) for {'C': 1000.0, 'kernel': 'linear'}
0.585 (+/-0.236) for {'C': 10000.0, 'kernel': 'linear'}
0.611 (+/-0.242) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.17 0.17 0.17 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 10.0, 'kernel': 'linear'}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6658653846153846
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.496 (+/-0.001) for {'C': 0.0003981071705534969, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.496 (+/-0.001) for {'C': 0.0003981071705534969, 'kernel': 'rbf', 'gamma': 0.021281390459827135}
0.496 (+/-0.001) for {'C': 0.0003981071705534969, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.496 (+/-0.001) for {'C': 0.0003981071705534969, 'kernel': 'rbf', 'gamma': 0.022080047330189013}
0.496 (+/-0.001) for {'C': 0.0003981071705534969, 'kernel': 'rbf', 'gamma': 0.022490546058357815}
0.496 (+/-0.001) for {'C': 0.0003981071705534969, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.496 (+/-0.001) for {'C': 0.0003981071705534969, 'kernel': 'rbf', 'gamma': 0.023334580622810044}
0.496 (+/-0.001) for {'C': 0.0003981071705534969, 'kernel': 'rbf', 'gamma': 0.023768402866248775}
0.496 (+/-0.001) for {'C': 0.0003981071705534969, 'kernel': 'rbf', 'gamma': 0.024210290467361794}
0.496 (+/-0.001) for {'C': 0.0003981071705534969, 'kernel': 'rbf', 'gamma': 0.0246603933723434}
0.496 (+/-0.001) for {'C': 0.0003981071705534969, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.496 (+/-0.001) for {'C': 0.003981071705534969, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.496 (+/-0.001) for {'C': 0.003981071705534969, 'kernel': 'rbf', 'gamma': 0.021281390459827135}
0.496 (+/-0.001) for {'C': 0.003981071705534969, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.496 (+/-0.001) for {'C': 0.003981071705534969, 'kernel': 'rbf', 'gamma': 0.022080047330189013}
0.496 (+/-0.001) for {'C': 0.003981071705534969, 'kernel': 'rbf', 'gamma': 0.022490546058357815}
0.496 (+/-0.001) for {'C': 0.003981071705534969, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.496 (+/-0.001) for {'C': 0.003981071705534969, 'kernel': 'rbf', 'gamma': 0.023334580622810044}
0.496 (+/-0.001) for {'C': 0.003981071705534969, 'kernel': 'rbf', 'gamma': 0.023768402866248775}
0.496 (+/-0.001) for {'C': 0.003981071705534969, 'kernel': 'rbf', 'gamma': 0.024210290467361794}
0.496 (+/-0.001) for {'C': 0.003981071705534969, 'kernel': 'rbf', 'gamma': 0.0246603933723434}
0.496 (+/-0.001) for {'C': 0.003981071705534969, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.496 (+/-0.001) for {'C': 0.03981071705534969, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.496 (+/-0.001) for {'C': 0.03981071705534969, 'kernel': 'rbf', 'gamma': 0.021281390459827135}
0.496 (+/-0.001) for {'C': 0.03981071705534969, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.496 (+/-0.001) for {'C': 0.03981071705534969, 'kernel': 'rbf', 'gamma': 0.022080047330189013}
0.496 (+/-0.001) for {'C': 0.03981071705534969, 'kernel': 'rbf', 'gamma': 0.022490546058357815}
0.496 (+/-0.001) for {'C': 0.03981071705534969, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.496 (+/-0.001) for {'C': 0.03981071705534969, 'kernel': 'rbf', 'gamma': 0.023334580622810044}
0.496 (+/-0.001) for {'C': 0.03981071705534969, 'kernel': 'rbf', 'gamma': 0.023768402866248775}
0.496 (+/-0.001) for {'C': 0.03981071705534969, 'kernel': 'rbf', 'gamma': 0.024210290467361794}
0.496 (+/-0.001) for {'C': 0.03981071705534969, 'kernel': 'rbf', 'gamma': 0.0246603933723434}
0.496 (+/-0.001) for {'C': 0.03981071705534969, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.747 (+/-0.502) for {'C': 0.3981071705534969, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.747 (+/-0.502) for {'C': 0.3981071705534969, 'kernel': 'rbf', 'gamma': 0.021281390459827135}
0.747 (+/-0.502) for {'C': 0.3981071705534969, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.747 (+/-0.502) for {'C': 0.3981071705534969, 'kernel': 'rbf', 'gamma': 0.022080047330189013}
0.747 (+/-0.502) for {'C': 0.3981071705534969, 'kernel': 'rbf', 'gamma': 0.022490546058357815}
0.747 (+/-0.502) for {'C': 0.3981071705534969, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.747 (+/-0.502) for {'C': 0.3981071705534969, 'kernel': 'rbf', 'gamma': 0.023334580622810044}
0.747 (+/-0.502) for {'C': 0.3981071705534969, 'kernel': 'rbf', 'gamma': 0.023768402866248775}
0.747 (+/-0.502) for {'C': 0.3981071705534969, 'kernel': 'rbf', 'gamma': 0.024210290467361794}
0.747 (+/-0.502) for {'C': 0.3981071705534969, 'kernel': 'rbf', 'gamma': 0.0246603933723434}
0.747 (+/-0.502) for {'C': 0.3981071705534969, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.747 (+/-0.502) for {'C': 3.981071705534969, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.747 (+/-0.502) for {'C': 3.981071705534969, 'kernel': 'rbf', 'gamma': 0.021281390459827135}
0.747 (+/-0.502) for {'C': 3.981071705534969, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.747 (+/-0.502) for {'C': 3.981071705534969, 'kernel': 'rbf', 'gamma': 0.022080047330189013}
0.747 (+/-0.502) for {'C': 3.981071705534969, 'kernel': 'rbf', 'gamma': 0.022490546058357815}
0.747 (+/-0.502) for {'C': 3.981071705534969, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.747 (+/-0.502) for {'C': 3.981071705534969, 'kernel': 'rbf', 'gamma': 0.023334580622810044}
0.747 (+/-0.502) for {'C': 3.981071705534969, 'kernel': 'rbf', 'gamma': 0.023768402866248775}
0.747 (+/-0.502) for {'C': 3.981071705534969, 'kernel': 'rbf', 'gamma': 0.024210290467361794}
0.747 (+/-0.502) for {'C': 3.981071705534969, 'kernel': 'rbf', 'gamma': 0.0246603933723434}
0.747 (+/-0.502) for {'C': 3.981071705534969, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.727 (+/-0.397) for {'C': 39.81071705534969, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.735 (+/-0.402) for {'C': 39.81071705534969, 'kernel': 'rbf', 'gamma': 0.021281390459827135}
0.735 (+/-0.402) for {'C': 39.81071705534969, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.735 (+/-0.402) for {'C': 39.81071705534969, 'kernel': 'rbf', 'gamma': 0.022080047330189013}
0.735 (+/-0.402) for {'C': 39.81071705534969, 'kernel': 'rbf', 'gamma': 0.022490546058357815}
0.735 (+/-0.402) for {'C': 39.81071705534969, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.710 (+/-0.363) for {'C': 39.81071705534969, 'kernel': 'rbf', 'gamma': 0.023334580622810044}
0.710 (+/-0.363) for {'C': 39.81071705534969, 'kernel': 'rbf', 'gamma': 0.023768402866248775}
0.660 (+/-0.327) for {'C': 39.81071705534969, 'kernel': 'rbf', 'gamma': 0.024210290467361794}
0.660 (+/-0.327) for {'C': 39.81071705534969, 'kernel': 'rbf', 'gamma': 0.0246603933723434}
0.664 (+/-0.326) for {'C': 39.81071705534969, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.658 (+/-0.388) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.658 (+/-0.388) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.021281390459827135}
0.658 (+/-0.388) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.658 (+/-0.388) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.022080047330189013}
0.658 (+/-0.388) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.022490546058357815}
0.658 (+/-0.388) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.658 (+/-0.388) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.023334580622810044}
0.658 (+/-0.388) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.023768402866248775}
0.658 (+/-0.388) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.024210290467361794}
0.658 (+/-0.388) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.0246603933723434}
0.658 (+/-0.388) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.627 (+/-0.313) for {'C': 3981.071705534969, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.629 (+/-0.312) for {'C': 3981.071705534969, 'kernel': 'rbf', 'gamma': 0.021281390459827135}
0.629 (+/-0.312) for {'C': 3981.071705534969, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.629 (+/-0.312) for {'C': 3981.071705534969, 'kernel': 'rbf', 'gamma': 0.022080047330189013}
0.629 (+/-0.312) for {'C': 3981.071705534969, 'kernel': 'rbf', 'gamma': 0.022490546058357815}
0.630 (+/-0.311) for {'C': 3981.071705534969, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.630 (+/-0.311) for {'C': 3981.071705534969, 'kernel': 'rbf', 'gamma': 0.023334580622810044}
0.630 (+/-0.311) for {'C': 3981.071705534969, 'kernel': 'rbf', 'gamma': 0.023768402866248775}
0.619 (+/-0.321) for {'C': 3981.071705534969, 'kernel': 'rbf', 'gamma': 0.024210290467361794}
0.619 (+/-0.321) for {'C': 3981.071705534969, 'kernel': 'rbf', 'gamma': 0.0246603933723434}
0.619 (+/-0.321) for {'C': 3981.071705534969, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.614 (+/-0.327) for {'C': 39810.71705534969, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.614 (+/-0.327) for {'C': 39810.71705534969, 'kernel': 'rbf', 'gamma': 0.021281390459827135}
0.614 (+/-0.327) for {'C': 39810.71705534969, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.614 (+/-0.327) for {'C': 39810.71705534969, 'kernel': 'rbf', 'gamma': 0.022080047330189013}
0.614 (+/-0.327) for {'C': 39810.71705534969, 'kernel': 'rbf', 'gamma': 0.022490546058357815}
0.614 (+/-0.327) for {'C': 39810.71705534969, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.614 (+/-0.327) for {'C': 39810.71705534969, 'kernel': 'rbf', 'gamma': 0.023334580622810044}
0.614 (+/-0.327) for {'C': 39810.71705534969, 'kernel': 'rbf', 'gamma': 0.023768402866248775}
0.614 (+/-0.327) for {'C': 39810.71705534969, 'kernel': 'rbf', 'gamma': 0.024210290467361794}
0.614 (+/-0.327) for {'C': 39810.71705534969, 'kernel': 'rbf', 'gamma': 0.0246603933723434}
0.614 (+/-0.327) for {'C': 39810.71705534969, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.614 (+/-0.327) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.614 (+/-0.327) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 0.021281390459827135}
0.614 (+/-0.327) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.614 (+/-0.327) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 0.022080047330189013}
0.614 (+/-0.327) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 0.022490546058357815}
0.614 (+/-0.327) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.614 (+/-0.327) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 0.023334580622810044}
0.614 (+/-0.327) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 0.023768402866248775}
0.614 (+/-0.327) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 0.024210290467361794}
0.614 (+/-0.327) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 0.0246603933723434}
0.614 (+/-0.327) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.614 (+/-0.327) for {'C': 3981071.7055349695, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.614 (+/-0.327) for {'C': 3981071.7055349695, 'kernel': 'rbf', 'gamma': 0.021281390459827135}
0.614 (+/-0.327) for {'C': 3981071.7055349695, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.614 (+/-0.327) for {'C': 3981071.7055349695, 'kernel': 'rbf', 'gamma': 0.022080047330189013}
0.614 (+/-0.327) for {'C': 3981071.7055349695, 'kernel': 'rbf', 'gamma': 0.022490546058357815}
0.614 (+/-0.327) for {'C': 3981071.7055349695, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.614 (+/-0.327) for {'C': 3981071.7055349695, 'kernel': 'rbf', 'gamma': 0.023334580622810044}
0.614 (+/-0.327) for {'C': 3981071.7055349695, 'kernel': 'rbf', 'gamma': 0.023768402866248775}
0.614 (+/-0.327) for {'C': 3981071.7055349695, 'kernel': 'rbf', 'gamma': 0.024210290467361794}
0.614 (+/-0.327) for {'C': 3981071.7055349695, 'kernel': 'rbf', 'gamma': 0.0246603933723434}
0.614 (+/-0.327) for {'C': 3981071.7055349695, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 1.0, 'kernel': 'linear'}
0.674 (+/-0.375) for {'C': 10.0, 'kernel': 'linear'}
0.631 (+/-0.312) for {'C': 100.0, 'kernel': 'linear'}
0.637 (+/-0.307) for {'C': 1000.0, 'kernel': 'linear'}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'linear'}
0.620 (+/-0.321) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.20 0.17 0.18 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.500 (+/-0.000) for {'C': 0.0003981071705534969, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.500 (+/-0.000) for {'C': 0.0003981071705534969, 'kernel': 'rbf', 'gamma': 0.021281390459827135}
0.500 (+/-0.000) for {'C': 0.0003981071705534969, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.500 (+/-0.000) for {'C': 0.0003981071705534969, 'kernel': 'rbf', 'gamma': 0.022080047330189013}
0.500 (+/-0.000) for {'C': 0.0003981071705534969, 'kernel': 'rbf', 'gamma': 0.022490546058357815}
0.500 (+/-0.000) for {'C': 0.0003981071705534969, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.500 (+/-0.000) for {'C': 0.0003981071705534969, 'kernel': 'rbf', 'gamma': 0.023334580622810044}
0.500 (+/-0.000) for {'C': 0.0003981071705534969, 'kernel': 'rbf', 'gamma': 0.023768402866248775}
0.500 (+/-0.000) for {'C': 0.0003981071705534969, 'kernel': 'rbf', 'gamma': 0.024210290467361794}
0.500 (+/-0.000) for {'C': 0.0003981071705534969, 'kernel': 'rbf', 'gamma': 0.0246603933723434}
0.500 (+/-0.000) for {'C': 0.0003981071705534969, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.500 (+/-0.000) for {'C': 0.003981071705534969, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.500 (+/-0.000) for {'C': 0.003981071705534969, 'kernel': 'rbf', 'gamma': 0.021281390459827135}
0.500 (+/-0.000) for {'C': 0.003981071705534969, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.500 (+/-0.000) for {'C': 0.003981071705534969, 'kernel': 'rbf', 'gamma': 0.022080047330189013}
0.500 (+/-0.000) for {'C': 0.003981071705534969, 'kernel': 'rbf', 'gamma': 0.022490546058357815}
0.500 (+/-0.000) for {'C': 0.003981071705534969, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.500 (+/-0.000) for {'C': 0.003981071705534969, 'kernel': 'rbf', 'gamma': 0.023334580622810044}
0.500 (+/-0.000) for {'C': 0.003981071705534969, 'kernel': 'rbf', 'gamma': 0.023768402866248775}
0.500 (+/-0.000) for {'C': 0.003981071705534969, 'kernel': 'rbf', 'gamma': 0.024210290467361794}
0.500 (+/-0.000) for {'C': 0.003981071705534969, 'kernel': 'rbf', 'gamma': 0.0246603933723434}
0.500 (+/-0.000) for {'C': 0.003981071705534969, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.500 (+/-0.000) for {'C': 0.03981071705534969, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.500 (+/-0.000) for {'C': 0.03981071705534969, 'kernel': 'rbf', 'gamma': 0.021281390459827135}
0.500 (+/-0.000) for {'C': 0.03981071705534969, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.500 (+/-0.000) for {'C': 0.03981071705534969, 'kernel': 'rbf', 'gamma': 0.022080047330189013}
0.500 (+/-0.000) for {'C': 0.03981071705534969, 'kernel': 'rbf', 'gamma': 0.022490546058357815}
0.500 (+/-0.000) for {'C': 0.03981071705534969, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.500 (+/-0.000) for {'C': 0.03981071705534969, 'kernel': 'rbf', 'gamma': 0.023334580622810044}
0.500 (+/-0.000) for {'C': 0.03981071705534969, 'kernel': 'rbf', 'gamma': 0.023768402866248775}
0.500 (+/-0.000) for {'C': 0.03981071705534969, 'kernel': 'rbf', 'gamma': 0.024210290467361794}
0.500 (+/-0.000) for {'C': 0.03981071705534969, 'kernel': 'rbf', 'gamma': 0.0246603933723434}
0.500 (+/-0.000) for {'C': 0.03981071705534969, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.617 (+/-0.238) for {'C': 0.3981071705534969, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.617 (+/-0.238) for {'C': 0.3981071705534969, 'kernel': 'rbf', 'gamma': 0.021281390459827135}
0.617 (+/-0.238) for {'C': 0.3981071705534969, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.617 (+/-0.238) for {'C': 0.3981071705534969, 'kernel': 'rbf', 'gamma': 0.022080047330189013}
0.617 (+/-0.238) for {'C': 0.3981071705534969, 'kernel': 'rbf', 'gamma': 0.022490546058357815}
0.617 (+/-0.238) for {'C': 0.3981071705534969, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.617 (+/-0.238) for {'C': 0.3981071705534969, 'kernel': 'rbf', 'gamma': 0.023334580622810044}
0.617 (+/-0.238) for {'C': 0.3981071705534969, 'kernel': 'rbf', 'gamma': 0.023768402866248775}
0.617 (+/-0.238) for {'C': 0.3981071705534969, 'kernel': 'rbf', 'gamma': 0.024210290467361794}
0.617 (+/-0.238) for {'C': 0.3981071705534969, 'kernel': 'rbf', 'gamma': 0.0246603933723434}
0.617 (+/-0.238) for {'C': 0.3981071705534969, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.617 (+/-0.238) for {'C': 3.981071705534969, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.617 (+/-0.238) for {'C': 3.981071705534969, 'kernel': 'rbf', 'gamma': 0.021281390459827135}
0.617 (+/-0.238) for {'C': 3.981071705534969, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.617 (+/-0.238) for {'C': 3.981071705534969, 'kernel': 'rbf', 'gamma': 0.022080047330189013}
0.617 (+/-0.238) for {'C': 3.981071705534969, 'kernel': 'rbf', 'gamma': 0.022490546058357815}
0.617 (+/-0.238) for {'C': 3.981071705534969, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.617 (+/-0.238) for {'C': 3.981071705534969, 'kernel': 'rbf', 'gamma': 0.023334580622810044}
0.617 (+/-0.238) for {'C': 3.981071705534969, 'kernel': 'rbf', 'gamma': 0.023768402866248775}
0.617 (+/-0.238) for {'C': 3.981071705534969, 'kernel': 'rbf', 'gamma': 0.024210290467361794}
0.617 (+/-0.238) for {'C': 3.981071705534969, 'kernel': 'rbf', 'gamma': 0.0246603933723434}
0.617 (+/-0.238) for {'C': 3.981071705534969, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.682 (+/-0.294) for {'C': 39.81071705534969, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.682 (+/-0.295) for {'C': 39.81071705534969, 'kernel': 'rbf', 'gamma': 0.021281390459827135}
0.682 (+/-0.295) for {'C': 39.81071705534969, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.682 (+/-0.295) for {'C': 39.81071705534969, 'kernel': 'rbf', 'gamma': 0.022080047330189013}
0.682 (+/-0.295) for {'C': 39.81071705534969, 'kernel': 'rbf', 'gamma': 0.022490546058357815}
0.682 (+/-0.295) for {'C': 39.81071705534969, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.682 (+/-0.295) for {'C': 39.81071705534969, 'kernel': 'rbf', 'gamma': 0.023334580622810044}
0.682 (+/-0.295) for {'C': 39.81071705534969, 'kernel': 'rbf', 'gamma': 0.023768402866248775}
0.665 (+/-0.314) for {'C': 39.81071705534969, 'kernel': 'rbf', 'gamma': 0.024210290467361794}
0.665 (+/-0.314) for {'C': 39.81071705534969, 'kernel': 'rbf', 'gamma': 0.0246603933723434}
0.666 (+/-0.315) for {'C': 39.81071705534969, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.658 (+/-0.314) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.658 (+/-0.314) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.021281390459827135}
0.658 (+/-0.314) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.658 (+/-0.315) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.022080047330189013}
0.658 (+/-0.315) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.022490546058357815}
0.658 (+/-0.315) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.658 (+/-0.314) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.023334580622810044}
0.658 (+/-0.314) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.023768402866248775}
0.658 (+/-0.314) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.024210290467361794}
0.658 (+/-0.314) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.0246603933723434}
0.658 (+/-0.314) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.636 (+/-0.237) for {'C': 3981.071705534969, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.637 (+/-0.237) for {'C': 3981.071705534969, 'kernel': 'rbf', 'gamma': 0.021281390459827135}
0.637 (+/-0.237) for {'C': 3981.071705534969, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.637 (+/-0.237) for {'C': 3981.071705534969, 'kernel': 'rbf', 'gamma': 0.022080047330189013}
0.637 (+/-0.237) for {'C': 3981.071705534969, 'kernel': 'rbf', 'gamma': 0.022490546058357815}
0.637 (+/-0.238) for {'C': 3981.071705534969, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.637 (+/-0.238) for {'C': 3981.071705534969, 'kernel': 'rbf', 'gamma': 0.023334580622810044}
0.637 (+/-0.238) for {'C': 3981.071705534969, 'kernel': 'rbf', 'gamma': 0.023768402866248775}
0.612 (+/-0.240) for {'C': 3981.071705534969, 'kernel': 'rbf', 'gamma': 0.024210290467361794}
0.612 (+/-0.240) for {'C': 3981.071705534969, 'kernel': 'rbf', 'gamma': 0.0246603933723434}
0.612 (+/-0.240) for {'C': 3981.071705534969, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.588 (+/-0.232) for {'C': 39810.71705534969, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.588 (+/-0.232) for {'C': 39810.71705534969, 'kernel': 'rbf', 'gamma': 0.021281390459827135}
0.588 (+/-0.232) for {'C': 39810.71705534969, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.588 (+/-0.232) for {'C': 39810.71705534969, 'kernel': 'rbf', 'gamma': 0.022080047330189013}
0.588 (+/-0.232) for {'C': 39810.71705534969, 'kernel': 'rbf', 'gamma': 0.022490546058357815}
0.588 (+/-0.233) for {'C': 39810.71705534969, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.588 (+/-0.233) for {'C': 39810.71705534969, 'kernel': 'rbf', 'gamma': 0.023334580622810044}
0.588 (+/-0.233) for {'C': 39810.71705534969, 'kernel': 'rbf', 'gamma': 0.023768402866248775}
0.587 (+/-0.233) for {'C': 39810.71705534969, 'kernel': 'rbf', 'gamma': 0.024210290467361794}
0.587 (+/-0.233) for {'C': 39810.71705534969, 'kernel': 'rbf', 'gamma': 0.0246603933723434}
0.587 (+/-0.234) for {'C': 39810.71705534969, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.587 (+/-0.235) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.587 (+/-0.235) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 0.021281390459827135}
0.587 (+/-0.235) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.587 (+/-0.235) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 0.022080047330189013}
0.587 (+/-0.235) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 0.022490546058357815}
0.587 (+/-0.235) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.587 (+/-0.235) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 0.023334580622810044}
0.587 (+/-0.235) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 0.023768402866248775}
0.586 (+/-0.235) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 0.024210290467361794}
0.587 (+/-0.235) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 0.0246603933723434}
0.587 (+/-0.235) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.587 (+/-0.235) for {'C': 3981071.7055349695, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.587 (+/-0.235) for {'C': 3981071.7055349695, 'kernel': 'rbf', 'gamma': 0.021281390459827135}
0.587 (+/-0.235) for {'C': 3981071.7055349695, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.587 (+/-0.235) for {'C': 3981071.7055349695, 'kernel': 'rbf', 'gamma': 0.022080047330189013}
0.587 (+/-0.235) for {'C': 3981071.7055349695, 'kernel': 'rbf', 'gamma': 0.022490546058357815}
0.587 (+/-0.235) for {'C': 3981071.7055349695, 'kernel': 'rbf', 'gamma': 0.02290867652767774}
0.587 (+/-0.235) for {'C': 3981071.7055349695, 'kernel': 'rbf', 'gamma': 0.023334580622810044}
0.587 (+/-0.235) for {'C': 3981071.7055349695, 'kernel': 'rbf', 'gamma': 0.023768402866248775}
0.586 (+/-0.235) for {'C': 3981071.7055349695, 'kernel': 'rbf', 'gamma': 0.024210290467361794}
0.587 (+/-0.235) for {'C': 3981071.7055349695, 'kernel': 'rbf', 'gamma': 0.0246603933723434}
0.587 (+/-0.235) for {'C': 3981071.7055349695, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.663 (+/-0.316) for {'C': 10.0, 'kernel': 'linear'}
0.661 (+/-0.315) for {'C': 100.0, 'kernel': 'linear'}
0.637 (+/-0.239) for {'C': 1000.0, 'kernel': 'linear'}
0.585 (+/-0.236) for {'C': 10000.0, 'kernel': 'linear'}
0.611 (+/-0.242) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.17 0.17 0.17 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 39.81071705534969, 'kernel': 'rbf', 'gamma': 0.021281390459827135}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6822115384615385
第1轮loop中,tunemodel函数得到的最优参数是: ([{'C': array([ 3.98107171, 6.30957344, 10. , 15.84893192,
25.11886432, 39.81071706, 63.09573445, 100. ,
158.48931925, 251.18864315, 398.10717055]), 'kernel': ['rbf'], 'gamma': array([0.02089296, 0.02097008, 0.02104747, 0.02112516, 0.02120313,
0.02128139, 0.02135994, 0.02143878, 0.02151791, 0.02159733,
0.02167704])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}], {'C': 39.81071705534969, 'kernel': 'rbf', 'gamma': 0.021281390459827135}, 0.6822115384615385)
这是第1次迭代微调C和gamma。
第1次迭代,得到delta: [1.42108547e-14 1.62728607e-03]
执行tunemodel()函数前,使用的grid search parameter是:
[{'C': array([ 3.98107171, 6.30957344, 10. , 15.84893192,
25.11886432, 39.81071706, 63.09573445, 100. ,
158.48931925, 251.18864315, 398.10717055]), 'kernel': ['rbf'], 'gamma': array([0.02089296, 0.02097008, 0.02104747, 0.02112516, 0.02120313,
0.02128139, 0.02135994, 0.02143878, 0.02151791, 0.02159733,
0.02167704])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.747 (+/-0.502) for {'C': 3.9810717055349696, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.747 (+/-0.502) for {'C': 3.9810717055349696, 'kernel': 'rbf', 'gamma': 0.02097007578544648}
0.747 (+/-0.502) for {'C': 3.9810717055349696, 'kernel': 'rbf', 'gamma': 0.02104747488655977}
0.747 (+/-0.502) for {'C': 3.9810717055349696, 'kernel': 'rbf', 'gamma': 0.021125159662408542}
0.747 (+/-0.502) for {'C': 3.9810717055349696, 'kernel': 'rbf', 'gamma': 0.021203131167398505}
0.747 (+/-0.502) for {'C': 3.9810717055349696, 'kernel': 'rbf', 'gamma': 0.02128139045982712}
0.747 (+/-0.502) for {'C': 3.9810717055349696, 'kernel': 'rbf', 'gamma': 0.02135993860189796}
0.747 (+/-0.502) for {'C': 3.9810717055349696, 'kernel': 'rbf', 'gamma': 0.021438776659735086}
0.747 (+/-0.502) for {'C': 3.9810717055349696, 'kernel': 'rbf', 'gamma': 0.02151790570339755}
0.747 (+/-0.502) for {'C': 3.9810717055349696, 'kernel': 'rbf', 'gamma': 0.021597326806893944}
0.747 (+/-0.502) for {'C': 3.9810717055349696, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.747 (+/-0.502) for {'C': 6.309573444801927, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.747 (+/-0.502) for {'C': 6.309573444801927, 'kernel': 'rbf', 'gamma': 0.02097007578544648}
0.747 (+/-0.502) for {'C': 6.309573444801927, 'kernel': 'rbf', 'gamma': 0.02104747488655977}
0.747 (+/-0.502) for {'C': 6.309573444801927, 'kernel': 'rbf', 'gamma': 0.021125159662408542}
0.747 (+/-0.502) for {'C': 6.309573444801927, 'kernel': 'rbf', 'gamma': 0.021203131167398505}
0.747 (+/-0.502) for {'C': 6.309573444801927, 'kernel': 'rbf', 'gamma': 0.02128139045982712}
0.747 (+/-0.502) for {'C': 6.309573444801927, 'kernel': 'rbf', 'gamma': 0.02135993860189796}
0.747 (+/-0.502) for {'C': 6.309573444801927, 'kernel': 'rbf', 'gamma': 0.021438776659735086}
0.747 (+/-0.502) for {'C': 6.309573444801927, 'kernel': 'rbf', 'gamma': 0.02151790570339755}
0.747 (+/-0.502) for {'C': 6.309573444801927, 'kernel': 'rbf', 'gamma': 0.021597326806893944}
0.747 (+/-0.502) for {'C': 6.309573444801927, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.747 (+/-0.502) for {'C': 9.99999999999999, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.747 (+/-0.502) for {'C': 9.99999999999999, 'kernel': 'rbf', 'gamma': 0.02097007578544648}
0.747 (+/-0.502) for {'C': 9.99999999999999, 'kernel': 'rbf', 'gamma': 0.02104747488655977}
0.747 (+/-0.502) for {'C': 9.99999999999999, 'kernel': 'rbf', 'gamma': 0.021125159662408542}
0.747 (+/-0.502) for {'C': 9.99999999999999, 'kernel': 'rbf', 'gamma': 0.021203131167398505}
0.747 (+/-0.502) for {'C': 9.99999999999999, 'kernel': 'rbf', 'gamma': 0.02128139045982712}
0.747 (+/-0.502) for {'C': 9.99999999999999, 'kernel': 'rbf', 'gamma': 0.02135993860189796}
0.747 (+/-0.502) for {'C': 9.99999999999999, 'kernel': 'rbf', 'gamma': 0.021438776659735086}
0.747 (+/-0.502) for {'C': 9.99999999999999, 'kernel': 'rbf', 'gamma': 0.02151790570339755}
0.747 (+/-0.502) for {'C': 9.99999999999999, 'kernel': 'rbf', 'gamma': 0.021597326806893944}
0.747 (+/-0.502) for {'C': 9.99999999999999, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.747 (+/-0.502) for {'C': 15.84893192461112, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.747 (+/-0.502) for {'C': 15.84893192461112, 'kernel': 'rbf', 'gamma': 0.02097007578544648}
0.747 (+/-0.502) for {'C': 15.84893192461112, 'kernel': 'rbf', 'gamma': 0.02104747488655977}
0.747 (+/-0.502) for {'C': 15.84893192461112, 'kernel': 'rbf', 'gamma': 0.021125159662408542}
0.747 (+/-0.502) for {'C': 15.84893192461112, 'kernel': 'rbf', 'gamma': 0.021203131167398505}
0.747 (+/-0.502) for {'C': 15.84893192461112, 'kernel': 'rbf', 'gamma': 0.02128139045982712}
0.747 (+/-0.502) for {'C': 15.84893192461112, 'kernel': 'rbf', 'gamma': 0.02135993860189796}
0.747 (+/-0.502) for {'C': 15.84893192461112, 'kernel': 'rbf', 'gamma': 0.021438776659735086}
0.747 (+/-0.502) for {'C': 15.84893192461112, 'kernel': 'rbf', 'gamma': 0.02151790570339755}
0.747 (+/-0.502) for {'C': 15.84893192461112, 'kernel': 'rbf', 'gamma': 0.021597326806893944}
0.747 (+/-0.502) for {'C': 15.84893192461112, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.747 (+/-0.502) for {'C': 25.11886431509578, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.747 (+/-0.502) for {'C': 25.11886431509578, 'kernel': 'rbf', 'gamma': 0.02097007578544648}
0.747 (+/-0.502) for {'C': 25.11886431509578, 'kernel': 'rbf', 'gamma': 0.02104747488655977}
0.747 (+/-0.502) for {'C': 25.11886431509578, 'kernel': 'rbf', 'gamma': 0.021125159662408542}
0.747 (+/-0.502) for {'C': 25.11886431509578, 'kernel': 'rbf', 'gamma': 0.021203131167398505}
0.747 (+/-0.502) for {'C': 25.11886431509578, 'kernel': 'rbf', 'gamma': 0.02128139045982712}
0.747 (+/-0.502) for {'C': 25.11886431509578, 'kernel': 'rbf', 'gamma': 0.02135993860189796}
0.747 (+/-0.502) for {'C': 25.11886431509578, 'kernel': 'rbf', 'gamma': 0.021438776659735086}
0.747 (+/-0.502) for {'C': 25.11886431509578, 'kernel': 'rbf', 'gamma': 0.02151790570339755}
0.747 (+/-0.502) for {'C': 25.11886431509578, 'kernel': 'rbf', 'gamma': 0.021597326806893944}
0.747 (+/-0.502) for {'C': 25.11886431509578, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.747 (+/-0.449) for {'C': 39.810717055349684, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.747 (+/-0.449) for {'C': 39.810717055349684, 'kernel': 'rbf', 'gamma': 0.02097007578544648}
0.747 (+/-0.449) for {'C': 39.810717055349684, 'kernel': 'rbf', 'gamma': 0.02104747488655977}
0.747 (+/-0.449) for {'C': 39.810717055349684, 'kernel': 'rbf', 'gamma': 0.021125159662408542}
0.747 (+/-0.449) for {'C': 39.810717055349684, 'kernel': 'rbf', 'gamma': 0.021203131167398505}
0.747 (+/-0.449) for {'C': 39.810717055349684, 'kernel': 'rbf', 'gamma': 0.02128139045982712}
0.747 (+/-0.449) for {'C': 39.810717055349684, 'kernel': 'rbf', 'gamma': 0.02135993860189796}
0.747 (+/-0.449) for {'C': 39.810717055349684, 'kernel': 'rbf', 'gamma': 0.021438776659735086}
0.747 (+/-0.449) for {'C': 39.810717055349684, 'kernel': 'rbf', 'gamma': 0.02151790570339755}
0.747 (+/-0.449) for {'C': 39.810717055349684, 'kernel': 'rbf', 'gamma': 0.021597326806893944}
0.747 (+/-0.449) for {'C': 39.810717055349684, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.748 (+/-0.449) for {'C': 63.09573444801924, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.748 (+/-0.449) for {'C': 63.09573444801924, 'kernel': 'rbf', 'gamma': 0.02097007578544648}
0.748 (+/-0.449) for {'C': 63.09573444801924, 'kernel': 'rbf', 'gamma': 0.02104747488655977}
0.748 (+/-0.449) for {'C': 63.09573444801924, 'kernel': 'rbf', 'gamma': 0.021125159662408542}
0.748 (+/-0.449) for {'C': 63.09573444801924, 'kernel': 'rbf', 'gamma': 0.021203131167398505}
0.748 (+/-0.449) for {'C': 63.09573444801924, 'kernel': 'rbf', 'gamma': 0.02128139045982712}
0.748 (+/-0.449) for {'C': 63.09573444801924, 'kernel': 'rbf', 'gamma': 0.02135993860189796}
0.748 (+/-0.449) for {'C': 63.09573444801924, 'kernel': 'rbf', 'gamma': 0.021438776659735086}
0.748 (+/-0.449) for {'C': 63.09573444801924, 'kernel': 'rbf', 'gamma': 0.02151790570339755}
0.748 (+/-0.449) for {'C': 63.09573444801924, 'kernel': 'rbf', 'gamma': 0.021597326806893944}
0.748 (+/-0.449) for {'C': 63.09573444801924, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.698 (+/-0.383) for {'C': 99.99999999999991, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.698 (+/-0.383) for {'C': 99.99999999999991, 'kernel': 'rbf', 'gamma': 0.02097007578544648}
0.698 (+/-0.383) for {'C': 99.99999999999991, 'kernel': 'rbf', 'gamma': 0.02104747488655977}
0.698 (+/-0.383) for {'C': 99.99999999999991, 'kernel': 'rbf', 'gamma': 0.021125159662408542}
0.698 (+/-0.383) for {'C': 99.99999999999991, 'kernel': 'rbf', 'gamma': 0.021203131167398505}
0.698 (+/-0.383) for {'C': 99.99999999999991, 'kernel': 'rbf', 'gamma': 0.02128139045982712}
0.698 (+/-0.383) for {'C': 99.99999999999991, 'kernel': 'rbf', 'gamma': 0.02135993860189796}
0.698 (+/-0.383) for {'C': 99.99999999999991, 'kernel': 'rbf', 'gamma': 0.021438776659735086}
0.698 (+/-0.383) for {'C': 99.99999999999991, 'kernel': 'rbf', 'gamma': 0.02151790570339755}
0.698 (+/-0.383) for {'C': 99.99999999999991, 'kernel': 'rbf', 'gamma': 0.021597326806893944}
0.698 (+/-0.383) for {'C': 99.99999999999991, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.698 (+/-0.383) for {'C': 158.4893192461112, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.698 (+/-0.383) for {'C': 158.4893192461112, 'kernel': 'rbf', 'gamma': 0.02097007578544648}
0.698 (+/-0.383) for {'C': 158.4893192461112, 'kernel': 'rbf', 'gamma': 0.02104747488655977}
0.698 (+/-0.383) for {'C': 158.4893192461112, 'kernel': 'rbf', 'gamma': 0.021125159662408542}
0.698 (+/-0.383) for {'C': 158.4893192461112, 'kernel': 'rbf', 'gamma': 0.021203131167398505}
0.698 (+/-0.383) for {'C': 158.4893192461112, 'kernel': 'rbf', 'gamma': 0.02128139045982712}
0.698 (+/-0.383) for {'C': 158.4893192461112, 'kernel': 'rbf', 'gamma': 0.02135993860189796}
0.698 (+/-0.383) for {'C': 158.4893192461112, 'kernel': 'rbf', 'gamma': 0.021438776659735086}
0.698 (+/-0.383) for {'C': 158.4893192461112, 'kernel': 'rbf', 'gamma': 0.02151790570339755}
0.698 (+/-0.383) for {'C': 158.4893192461112, 'kernel': 'rbf', 'gamma': 0.021597326806893944}
0.698 (+/-0.383) for {'C': 158.4893192461112, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.697 (+/-0.375) for {'C': 251.1886431509577, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.697 (+/-0.375) for {'C': 251.1886431509577, 'kernel': 'rbf', 'gamma': 0.02097007578544648}
0.697 (+/-0.375) for {'C': 251.1886431509577, 'kernel': 'rbf', 'gamma': 0.02104747488655977}
0.697 (+/-0.375) for {'C': 251.1886431509577, 'kernel': 'rbf', 'gamma': 0.021125159662408542}
0.697 (+/-0.375) for {'C': 251.1886431509577, 'kernel': 'rbf', 'gamma': 0.021203131167398505}
0.697 (+/-0.375) for {'C': 251.1886431509577, 'kernel': 'rbf', 'gamma': 0.02128139045982712}
0.697 (+/-0.375) for {'C': 251.1886431509577, 'kernel': 'rbf', 'gamma': 0.02135993860189796}
0.697 (+/-0.375) for {'C': 251.1886431509577, 'kernel': 'rbf', 'gamma': 0.021438776659735086}
0.697 (+/-0.375) for {'C': 251.1886431509577, 'kernel': 'rbf', 'gamma': 0.02151790570339755}
0.697 (+/-0.375) for {'C': 251.1886431509577, 'kernel': 'rbf', 'gamma': 0.021597326806893944}
0.697 (+/-0.375) for {'C': 251.1886431509577, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.722 (+/-0.417) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.722 (+/-0.417) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.02097007578544648}
0.722 (+/-0.417) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.02104747488655977}
0.722 (+/-0.417) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.021125159662408542}
0.722 (+/-0.417) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.021203131167398505}
0.722 (+/-0.417) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.02128139045982712}
0.722 (+/-0.417) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.02135993860189796}
0.722 (+/-0.417) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.021438776659735086}
0.722 (+/-0.417) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.02151790570339755}
0.722 (+/-0.417) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.021597326806893944}
0.722 (+/-0.417) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'linear'}
0.706 (+/-0.383) for {'C': 10.0, 'kernel': 'linear'}
0.643 (+/-0.320) for {'C': 100.0, 'kernel': 'linear'}
0.637 (+/-0.307) for {'C': 1000.0, 'kernel': 'linear'}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'linear'}
0.620 (+/-0.321) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.20 0.17 0.18 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.617 (+/-0.238) for {'C': 3.9810717055349696, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.617 (+/-0.238) for {'C': 3.9810717055349696, 'kernel': 'rbf', 'gamma': 0.02097007578544648}
0.617 (+/-0.238) for {'C': 3.9810717055349696, 'kernel': 'rbf', 'gamma': 0.02104747488655977}
0.617 (+/-0.238) for {'C': 3.9810717055349696, 'kernel': 'rbf', 'gamma': 0.021125159662408542}
0.617 (+/-0.238) for {'C': 3.9810717055349696, 'kernel': 'rbf', 'gamma': 0.021203131167398505}
0.617 (+/-0.238) for {'C': 3.9810717055349696, 'kernel': 'rbf', 'gamma': 0.02128139045982712}
0.617 (+/-0.238) for {'C': 3.9810717055349696, 'kernel': 'rbf', 'gamma': 0.02135993860189796}
0.617 (+/-0.238) for {'C': 3.9810717055349696, 'kernel': 'rbf', 'gamma': 0.021438776659735086}
0.617 (+/-0.238) for {'C': 3.9810717055349696, 'kernel': 'rbf', 'gamma': 0.02151790570339755}
0.617 (+/-0.238) for {'C': 3.9810717055349696, 'kernel': 'rbf', 'gamma': 0.021597326806893944}
0.617 (+/-0.238) for {'C': 3.9810717055349696, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.617 (+/-0.238) for {'C': 6.309573444801927, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.617 (+/-0.238) for {'C': 6.309573444801927, 'kernel': 'rbf', 'gamma': 0.02097007578544648}
0.617 (+/-0.238) for {'C': 6.309573444801927, 'kernel': 'rbf', 'gamma': 0.02104747488655977}
0.617 (+/-0.238) for {'C': 6.309573444801927, 'kernel': 'rbf', 'gamma': 0.021125159662408542}
0.617 (+/-0.238) for {'C': 6.309573444801927, 'kernel': 'rbf', 'gamma': 0.021203131167398505}
0.617 (+/-0.238) for {'C': 6.309573444801927, 'kernel': 'rbf', 'gamma': 0.02128139045982712}
0.617 (+/-0.238) for {'C': 6.309573444801927, 'kernel': 'rbf', 'gamma': 0.02135993860189796}
0.617 (+/-0.238) for {'C': 6.309573444801927, 'kernel': 'rbf', 'gamma': 0.021438776659735086}
0.617 (+/-0.238) for {'C': 6.309573444801927, 'kernel': 'rbf', 'gamma': 0.02151790570339755}
0.617 (+/-0.238) for {'C': 6.309573444801927, 'kernel': 'rbf', 'gamma': 0.021597326806893944}
0.617 (+/-0.238) for {'C': 6.309573444801927, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.617 (+/-0.238) for {'C': 9.99999999999999, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.617 (+/-0.238) for {'C': 9.99999999999999, 'kernel': 'rbf', 'gamma': 0.02097007578544648}
0.617 (+/-0.238) for {'C': 9.99999999999999, 'kernel': 'rbf', 'gamma': 0.02104747488655977}
0.617 (+/-0.238) for {'C': 9.99999999999999, 'kernel': 'rbf', 'gamma': 0.021125159662408542}
0.617 (+/-0.238) for {'C': 9.99999999999999, 'kernel': 'rbf', 'gamma': 0.021203131167398505}
0.617 (+/-0.238) for {'C': 9.99999999999999, 'kernel': 'rbf', 'gamma': 0.02128139045982712}
0.617 (+/-0.238) for {'C': 9.99999999999999, 'kernel': 'rbf', 'gamma': 0.02135993860189796}
0.617 (+/-0.238) for {'C': 9.99999999999999, 'kernel': 'rbf', 'gamma': 0.021438776659735086}
0.617 (+/-0.238) for {'C': 9.99999999999999, 'kernel': 'rbf', 'gamma': 0.02151790570339755}
0.617 (+/-0.238) for {'C': 9.99999999999999, 'kernel': 'rbf', 'gamma': 0.021597326806893944}
0.617 (+/-0.238) for {'C': 9.99999999999999, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.617 (+/-0.238) for {'C': 15.84893192461112, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.617 (+/-0.238) for {'C': 15.84893192461112, 'kernel': 'rbf', 'gamma': 0.02097007578544648}
0.617 (+/-0.238) for {'C': 15.84893192461112, 'kernel': 'rbf', 'gamma': 0.02104747488655977}
0.617 (+/-0.238) for {'C': 15.84893192461112, 'kernel': 'rbf', 'gamma': 0.021125159662408542}
0.617 (+/-0.238) for {'C': 15.84893192461112, 'kernel': 'rbf', 'gamma': 0.021203131167398505}
0.617 (+/-0.238) for {'C': 15.84893192461112, 'kernel': 'rbf', 'gamma': 0.02128139045982712}
0.617 (+/-0.238) for {'C': 15.84893192461112, 'kernel': 'rbf', 'gamma': 0.02135993860189796}
0.617 (+/-0.238) for {'C': 15.84893192461112, 'kernel': 'rbf', 'gamma': 0.021438776659735086}
0.617 (+/-0.238) for {'C': 15.84893192461112, 'kernel': 'rbf', 'gamma': 0.02151790570339755}
0.617 (+/-0.238) for {'C': 15.84893192461112, 'kernel': 'rbf', 'gamma': 0.021597326806893944}
0.617 (+/-0.238) for {'C': 15.84893192461112, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.617 (+/-0.238) for {'C': 25.11886431509578, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.617 (+/-0.238) for {'C': 25.11886431509578, 'kernel': 'rbf', 'gamma': 0.02097007578544648}
0.617 (+/-0.238) for {'C': 25.11886431509578, 'kernel': 'rbf', 'gamma': 0.02104747488655977}
0.617 (+/-0.238) for {'C': 25.11886431509578, 'kernel': 'rbf', 'gamma': 0.021125159662408542}
0.617 (+/-0.238) for {'C': 25.11886431509578, 'kernel': 'rbf', 'gamma': 0.021203131167398505}
0.617 (+/-0.238) for {'C': 25.11886431509578, 'kernel': 'rbf', 'gamma': 0.02128139045982712}
0.617 (+/-0.238) for {'C': 25.11886431509578, 'kernel': 'rbf', 'gamma': 0.02135993860189796}
0.617 (+/-0.238) for {'C': 25.11886431509578, 'kernel': 'rbf', 'gamma': 0.021438776659735086}
0.617 (+/-0.238) for {'C': 25.11886431509578, 'kernel': 'rbf', 'gamma': 0.02151790570339755}
0.617 (+/-0.238) for {'C': 25.11886431509578, 'kernel': 'rbf', 'gamma': 0.021597326806893944}
0.617 (+/-0.238) for {'C': 25.11886431509578, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.641 (+/-0.236) for {'C': 39.810717055349684, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.641 (+/-0.236) for {'C': 39.810717055349684, 'kernel': 'rbf', 'gamma': 0.02097007578544648}
0.641 (+/-0.236) for {'C': 39.810717055349684, 'kernel': 'rbf', 'gamma': 0.02104747488655977}
0.641 (+/-0.236) for {'C': 39.810717055349684, 'kernel': 'rbf', 'gamma': 0.021125159662408542}
0.641 (+/-0.236) for {'C': 39.810717055349684, 'kernel': 'rbf', 'gamma': 0.021203131167398505}
0.641 (+/-0.236) for {'C': 39.810717055349684, 'kernel': 'rbf', 'gamma': 0.02128139045982712}
0.641 (+/-0.236) for {'C': 39.810717055349684, 'kernel': 'rbf', 'gamma': 0.02135993860189796}
0.641 (+/-0.236) for {'C': 39.810717055349684, 'kernel': 'rbf', 'gamma': 0.021438776659735086}
0.641 (+/-0.236) for {'C': 39.810717055349684, 'kernel': 'rbf', 'gamma': 0.02151790570339755}
0.641 (+/-0.236) for {'C': 39.810717055349684, 'kernel': 'rbf', 'gamma': 0.021597326806893944}
0.641 (+/-0.236) for {'C': 39.810717055349684, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.666 (+/-0.316) for {'C': 63.09573444801924, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.666 (+/-0.316) for {'C': 63.09573444801924, 'kernel': 'rbf', 'gamma': 0.02097007578544648}
0.666 (+/-0.316) for {'C': 63.09573444801924, 'kernel': 'rbf', 'gamma': 0.02104747488655977}
0.666 (+/-0.316) for {'C': 63.09573444801924, 'kernel': 'rbf', 'gamma': 0.021125159662408542}
0.666 (+/-0.316) for {'C': 63.09573444801924, 'kernel': 'rbf', 'gamma': 0.021203131167398505}
0.666 (+/-0.316) for {'C': 63.09573444801924, 'kernel': 'rbf', 'gamma': 0.02128139045982712}
0.666 (+/-0.316) for {'C': 63.09573444801924, 'kernel': 'rbf', 'gamma': 0.02135993860189796}
0.666 (+/-0.316) for {'C': 63.09573444801924, 'kernel': 'rbf', 'gamma': 0.021438776659735086}
0.666 (+/-0.316) for {'C': 63.09573444801924, 'kernel': 'rbf', 'gamma': 0.02151790570339755}
0.666 (+/-0.316) for {'C': 63.09573444801924, 'kernel': 'rbf', 'gamma': 0.021597326806893944}
0.666 (+/-0.316) for {'C': 63.09573444801924, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.666 (+/-0.315) for {'C': 99.99999999999991, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.666 (+/-0.315) for {'C': 99.99999999999991, 'kernel': 'rbf', 'gamma': 0.02097007578544648}
0.666 (+/-0.315) for {'C': 99.99999999999991, 'kernel': 'rbf', 'gamma': 0.02104747488655977}
0.666 (+/-0.315) for {'C': 99.99999999999991, 'kernel': 'rbf', 'gamma': 0.021125159662408542}
0.666 (+/-0.315) for {'C': 99.99999999999991, 'kernel': 'rbf', 'gamma': 0.021203131167398505}
0.666 (+/-0.315) for {'C': 99.99999999999991, 'kernel': 'rbf', 'gamma': 0.02128139045982712}
0.666 (+/-0.315) for {'C': 99.99999999999991, 'kernel': 'rbf', 'gamma': 0.02135993860189796}
0.666 (+/-0.315) for {'C': 99.99999999999991, 'kernel': 'rbf', 'gamma': 0.021438776659735086}
0.666 (+/-0.315) for {'C': 99.99999999999991, 'kernel': 'rbf', 'gamma': 0.02151790570339755}
0.666 (+/-0.315) for {'C': 99.99999999999991, 'kernel': 'rbf', 'gamma': 0.021597326806893944}
0.666 (+/-0.315) for {'C': 99.99999999999991, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.666 (+/-0.315) for {'C': 158.4893192461112, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.666 (+/-0.315) for {'C': 158.4893192461112, 'kernel': 'rbf', 'gamma': 0.02097007578544648}
0.666 (+/-0.315) for {'C': 158.4893192461112, 'kernel': 'rbf', 'gamma': 0.02104747488655977}
0.666 (+/-0.315) for {'C': 158.4893192461112, 'kernel': 'rbf', 'gamma': 0.021125159662408542}
0.666 (+/-0.315) for {'C': 158.4893192461112, 'kernel': 'rbf', 'gamma': 0.021203131167398505}
0.666 (+/-0.315) for {'C': 158.4893192461112, 'kernel': 'rbf', 'gamma': 0.02128139045982712}
0.666 (+/-0.315) for {'C': 158.4893192461112, 'kernel': 'rbf', 'gamma': 0.02135993860189796}
0.666 (+/-0.315) for {'C': 158.4893192461112, 'kernel': 'rbf', 'gamma': 0.021438776659735086}
0.666 (+/-0.315) for {'C': 158.4893192461112, 'kernel': 'rbf', 'gamma': 0.02151790570339755}
0.666 (+/-0.315) for {'C': 158.4893192461112, 'kernel': 'rbf', 'gamma': 0.021597326806893944}
0.666 (+/-0.315) for {'C': 158.4893192461112, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.641 (+/-0.236) for {'C': 251.1886431509577, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.641 (+/-0.236) for {'C': 251.1886431509577, 'kernel': 'rbf', 'gamma': 0.02097007578544648}
0.641 (+/-0.236) for {'C': 251.1886431509577, 'kernel': 'rbf', 'gamma': 0.02104747488655977}
0.641 (+/-0.236) for {'C': 251.1886431509577, 'kernel': 'rbf', 'gamma': 0.021125159662408542}
0.641 (+/-0.236) for {'C': 251.1886431509577, 'kernel': 'rbf', 'gamma': 0.021203131167398505}
0.641 (+/-0.236) for {'C': 251.1886431509577, 'kernel': 'rbf', 'gamma': 0.02128139045982712}
0.641 (+/-0.236) for {'C': 251.1886431509577, 'kernel': 'rbf', 'gamma': 0.02135993860189796}
0.641 (+/-0.236) for {'C': 251.1886431509577, 'kernel': 'rbf', 'gamma': 0.021438776659735086}
0.641 (+/-0.236) for {'C': 251.1886431509577, 'kernel': 'rbf', 'gamma': 0.02151790570339755}
0.641 (+/-0.236) for {'C': 251.1886431509577, 'kernel': 'rbf', 'gamma': 0.021597326806893944}
0.641 (+/-0.236) for {'C': 251.1886431509577, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.641 (+/-0.237) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.641 (+/-0.237) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.02097007578544648}
0.641 (+/-0.237) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.02104747488655977}
0.641 (+/-0.237) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.021125159662408542}
0.641 (+/-0.237) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.021203131167398505}
0.641 (+/-0.237) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.02128139045982712}
0.641 (+/-0.237) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.02135993860189796}
0.641 (+/-0.237) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.021438776659735086}
0.641 (+/-0.237) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.02151790570339755}
0.641 (+/-0.237) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.021597326806893944}
0.641 (+/-0.237) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'linear'}
0.666 (+/-0.315) for {'C': 10.0, 'kernel': 'linear'}
0.637 (+/-0.237) for {'C': 100.0, 'kernel': 'linear'}
0.637 (+/-0.239) for {'C': 1000.0, 'kernel': 'linear'}
0.585 (+/-0.236) for {'C': 10000.0, 'kernel': 'linear'}
0.611 (+/-0.242) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.20 0.17 0.18 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.99 0.99 629
本轮grid search结果,得到最好的参数选择是: {'C': 63.09573444801924, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6663461538461538
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.747 (+/-0.502) for {'C': 3.9810717055349696, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.747 (+/-0.502) for {'C': 3.9810717055349696, 'kernel': 'rbf', 'gamma': 0.02097007578544648}
0.747 (+/-0.502) for {'C': 3.9810717055349696, 'kernel': 'rbf', 'gamma': 0.02104747488655977}
0.747 (+/-0.502) for {'C': 3.9810717055349696, 'kernel': 'rbf', 'gamma': 0.021125159662408542}
0.747 (+/-0.502) for {'C': 3.9810717055349696, 'kernel': 'rbf', 'gamma': 0.021203131167398505}
0.747 (+/-0.502) for {'C': 3.9810717055349696, 'kernel': 'rbf', 'gamma': 0.02128139045982712}
0.747 (+/-0.502) for {'C': 3.9810717055349696, 'kernel': 'rbf', 'gamma': 0.02135993860189796}
0.747 (+/-0.502) for {'C': 3.9810717055349696, 'kernel': 'rbf', 'gamma': 0.021438776659735086}
0.747 (+/-0.502) for {'C': 3.9810717055349696, 'kernel': 'rbf', 'gamma': 0.02151790570339755}
0.747 (+/-0.502) for {'C': 3.9810717055349696, 'kernel': 'rbf', 'gamma': 0.021597326806893944}
0.747 (+/-0.502) for {'C': 3.9810717055349696, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.747 (+/-0.502) for {'C': 6.309573444801927, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.747 (+/-0.502) for {'C': 6.309573444801927, 'kernel': 'rbf', 'gamma': 0.02097007578544648}
0.747 (+/-0.502) for {'C': 6.309573444801927, 'kernel': 'rbf', 'gamma': 0.02104747488655977}
0.747 (+/-0.502) for {'C': 6.309573444801927, 'kernel': 'rbf', 'gamma': 0.021125159662408542}
0.747 (+/-0.502) for {'C': 6.309573444801927, 'kernel': 'rbf', 'gamma': 0.021203131167398505}
0.747 (+/-0.502) for {'C': 6.309573444801927, 'kernel': 'rbf', 'gamma': 0.02128139045982712}
0.747 (+/-0.502) for {'C': 6.309573444801927, 'kernel': 'rbf', 'gamma': 0.02135993860189796}
0.747 (+/-0.502) for {'C': 6.309573444801927, 'kernel': 'rbf', 'gamma': 0.021438776659735086}
0.747 (+/-0.502) for {'C': 6.309573444801927, 'kernel': 'rbf', 'gamma': 0.02151790570339755}
0.747 (+/-0.502) for {'C': 6.309573444801927, 'kernel': 'rbf', 'gamma': 0.021597326806893944}
0.747 (+/-0.502) for {'C': 6.309573444801927, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.797 (+/-0.492) for {'C': 9.99999999999999, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.797 (+/-0.492) for {'C': 9.99999999999999, 'kernel': 'rbf', 'gamma': 0.02097007578544648}
0.797 (+/-0.492) for {'C': 9.99999999999999, 'kernel': 'rbf', 'gamma': 0.02104747488655977}
0.797 (+/-0.492) for {'C': 9.99999999999999, 'kernel': 'rbf', 'gamma': 0.021125159662408542}
0.797 (+/-0.492) for {'C': 9.99999999999999, 'kernel': 'rbf', 'gamma': 0.021203131167398505}
0.797 (+/-0.492) for {'C': 9.99999999999999, 'kernel': 'rbf', 'gamma': 0.02128139045982712}
0.797 (+/-0.492) for {'C': 9.99999999999999, 'kernel': 'rbf', 'gamma': 0.02135993860189796}
0.797 (+/-0.492) for {'C': 9.99999999999999, 'kernel': 'rbf', 'gamma': 0.021438776659735086}
0.797 (+/-0.492) for {'C': 9.99999999999999, 'kernel': 'rbf', 'gamma': 0.02151790570339755}
0.797 (+/-0.492) for {'C': 9.99999999999999, 'kernel': 'rbf', 'gamma': 0.021597326806893944}
0.797 (+/-0.492) for {'C': 9.99999999999999, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.748 (+/-0.449) for {'C': 15.84893192461112, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.748 (+/-0.449) for {'C': 15.84893192461112, 'kernel': 'rbf', 'gamma': 0.02097007578544648}
0.748 (+/-0.449) for {'C': 15.84893192461112, 'kernel': 'rbf', 'gamma': 0.02104747488655977}
0.748 (+/-0.449) for {'C': 15.84893192461112, 'kernel': 'rbf', 'gamma': 0.021125159662408542}
0.748 (+/-0.449) for {'C': 15.84893192461112, 'kernel': 'rbf', 'gamma': 0.021203131167398505}
0.748 (+/-0.449) for {'C': 15.84893192461112, 'kernel': 'rbf', 'gamma': 0.02128139045982712}
0.748 (+/-0.449) for {'C': 15.84893192461112, 'kernel': 'rbf', 'gamma': 0.02135993860189796}
0.748 (+/-0.449) for {'C': 15.84893192461112, 'kernel': 'rbf', 'gamma': 0.021438776659735086}
0.748 (+/-0.449) for {'C': 15.84893192461112, 'kernel': 'rbf', 'gamma': 0.02151790570339755}
0.748 (+/-0.449) for {'C': 15.84893192461112, 'kernel': 'rbf', 'gamma': 0.021597326806893944}
0.748 (+/-0.449) for {'C': 15.84893192461112, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.748 (+/-0.449) for {'C': 25.11886431509578, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.748 (+/-0.449) for {'C': 25.11886431509578, 'kernel': 'rbf', 'gamma': 0.02097007578544648}
0.748 (+/-0.449) for {'C': 25.11886431509578, 'kernel': 'rbf', 'gamma': 0.02104747488655977}
0.748 (+/-0.449) for {'C': 25.11886431509578, 'kernel': 'rbf', 'gamma': 0.021125159662408542}
0.748 (+/-0.449) for {'C': 25.11886431509578, 'kernel': 'rbf', 'gamma': 0.021203131167398505}
0.748 (+/-0.449) for {'C': 25.11886431509578, 'kernel': 'rbf', 'gamma': 0.02128139045982712}
0.748 (+/-0.449) for {'C': 25.11886431509578, 'kernel': 'rbf', 'gamma': 0.02135993860189796}
0.748 (+/-0.449) for {'C': 25.11886431509578, 'kernel': 'rbf', 'gamma': 0.021438776659735086}
0.748 (+/-0.449) for {'C': 25.11886431509578, 'kernel': 'rbf', 'gamma': 0.02151790570339755}
0.748 (+/-0.449) for {'C': 25.11886431509578, 'kernel': 'rbf', 'gamma': 0.021597326806893944}
0.748 (+/-0.449) for {'C': 25.11886431509578, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.727 (+/-0.397) for {'C': 39.810717055349684, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.727 (+/-0.397) for {'C': 39.810717055349684, 'kernel': 'rbf', 'gamma': 0.02097007578544648}
0.727 (+/-0.397) for {'C': 39.810717055349684, 'kernel': 'rbf', 'gamma': 0.02104747488655977}
0.727 (+/-0.397) for {'C': 39.810717055349684, 'kernel': 'rbf', 'gamma': 0.021125159662408542}
0.735 (+/-0.402) for {'C': 39.810717055349684, 'kernel': 'rbf', 'gamma': 0.021203131167398505}
0.735 (+/-0.402) for {'C': 39.810717055349684, 'kernel': 'rbf', 'gamma': 0.02128139045982712}
0.735 (+/-0.402) for {'C': 39.810717055349684, 'kernel': 'rbf', 'gamma': 0.02135993860189796}
0.735 (+/-0.402) for {'C': 39.810717055349684, 'kernel': 'rbf', 'gamma': 0.021438776659735086}
0.735 (+/-0.402) for {'C': 39.810717055349684, 'kernel': 'rbf', 'gamma': 0.02151790570339755}
0.735 (+/-0.402) for {'C': 39.810717055349684, 'kernel': 'rbf', 'gamma': 0.021597326806893944}
0.735 (+/-0.402) for {'C': 39.810717055349684, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.673 (+/-0.330) for {'C': 63.09573444801924, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.673 (+/-0.330) for {'C': 63.09573444801924, 'kernel': 'rbf', 'gamma': 0.02097007578544648}
0.673 (+/-0.330) for {'C': 63.09573444801924, 'kernel': 'rbf', 'gamma': 0.02104747488655977}
0.673 (+/-0.330) for {'C': 63.09573444801924, 'kernel': 'rbf', 'gamma': 0.021125159662408542}
0.673 (+/-0.330) for {'C': 63.09573444801924, 'kernel': 'rbf', 'gamma': 0.021203131167398505}
0.673 (+/-0.330) for {'C': 63.09573444801924, 'kernel': 'rbf', 'gamma': 0.02128139045982712}
0.673 (+/-0.330) for {'C': 63.09573444801924, 'kernel': 'rbf', 'gamma': 0.02135993860189796}
0.673 (+/-0.330) for {'C': 63.09573444801924, 'kernel': 'rbf', 'gamma': 0.021438776659735086}
0.673 (+/-0.330) for {'C': 63.09573444801924, 'kernel': 'rbf', 'gamma': 0.02151790570339755}
0.673 (+/-0.330) for {'C': 63.09573444801924, 'kernel': 'rbf', 'gamma': 0.021597326806893944}
0.673 (+/-0.330) for {'C': 63.09573444801924, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.664 (+/-0.317) for {'C': 99.99999999999991, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.664 (+/-0.317) for {'C': 99.99999999999991, 'kernel': 'rbf', 'gamma': 0.02097007578544648}
0.664 (+/-0.317) for {'C': 99.99999999999991, 'kernel': 'rbf', 'gamma': 0.02104747488655977}
0.664 (+/-0.317) for {'C': 99.99999999999991, 'kernel': 'rbf', 'gamma': 0.021125159662408542}
0.664 (+/-0.317) for {'C': 99.99999999999991, 'kernel': 'rbf', 'gamma': 0.021203131167398505}
0.664 (+/-0.317) for {'C': 99.99999999999991, 'kernel': 'rbf', 'gamma': 0.02128139045982712}
0.664 (+/-0.317) for {'C': 99.99999999999991, 'kernel': 'rbf', 'gamma': 0.02135993860189796}
0.664 (+/-0.317) for {'C': 99.99999999999991, 'kernel': 'rbf', 'gamma': 0.021438776659735086}
0.664 (+/-0.317) for {'C': 99.99999999999991, 'kernel': 'rbf', 'gamma': 0.02151790570339755}
0.664 (+/-0.317) for {'C': 99.99999999999991, 'kernel': 'rbf', 'gamma': 0.021597326806893944}
0.664 (+/-0.317) for {'C': 99.99999999999991, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.672 (+/-0.371) for {'C': 158.4893192461112, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.672 (+/-0.371) for {'C': 158.4893192461112, 'kernel': 'rbf', 'gamma': 0.02097007578544648}
0.672 (+/-0.371) for {'C': 158.4893192461112, 'kernel': 'rbf', 'gamma': 0.02104747488655977}
0.672 (+/-0.371) for {'C': 158.4893192461112, 'kernel': 'rbf', 'gamma': 0.021125159662408542}
0.672 (+/-0.371) for {'C': 158.4893192461112, 'kernel': 'rbf', 'gamma': 0.021203131167398505}
0.672 (+/-0.371) for {'C': 158.4893192461112, 'kernel': 'rbf', 'gamma': 0.02128139045982712}
0.672 (+/-0.371) for {'C': 158.4893192461112, 'kernel': 'rbf', 'gamma': 0.02135993860189796}
0.672 (+/-0.371) for {'C': 158.4893192461112, 'kernel': 'rbf', 'gamma': 0.021438776659735086}
0.672 (+/-0.371) for {'C': 158.4893192461112, 'kernel': 'rbf', 'gamma': 0.02151790570339755}
0.672 (+/-0.371) for {'C': 158.4893192461112, 'kernel': 'rbf', 'gamma': 0.021597326806893944}
0.672 (+/-0.371) for {'C': 158.4893192461112, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.669 (+/-0.379) for {'C': 251.1886431509577, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.669 (+/-0.379) for {'C': 251.1886431509577, 'kernel': 'rbf', 'gamma': 0.02097007578544648}
0.669 (+/-0.379) for {'C': 251.1886431509577, 'kernel': 'rbf', 'gamma': 0.02104747488655977}
0.669 (+/-0.379) for {'C': 251.1886431509577, 'kernel': 'rbf', 'gamma': 0.021125159662408542}
0.669 (+/-0.379) for {'C': 251.1886431509577, 'kernel': 'rbf', 'gamma': 0.021203131167398505}
0.666 (+/-0.380) for {'C': 251.1886431509577, 'kernel': 'rbf', 'gamma': 0.02128139045982712}
0.666 (+/-0.380) for {'C': 251.1886431509577, 'kernel': 'rbf', 'gamma': 0.02135993860189796}
0.666 (+/-0.381) for {'C': 251.1886431509577, 'kernel': 'rbf', 'gamma': 0.021438776659735086}
0.666 (+/-0.381) for {'C': 251.1886431509577, 'kernel': 'rbf', 'gamma': 0.02151790570339755}
0.666 (+/-0.381) for {'C': 251.1886431509577, 'kernel': 'rbf', 'gamma': 0.021597326806893944}
0.666 (+/-0.381) for {'C': 251.1886431509577, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.658 (+/-0.388) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.658 (+/-0.388) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.02097007578544648}
0.658 (+/-0.388) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.02104747488655977}
0.658 (+/-0.388) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.021125159662408542}
0.658 (+/-0.388) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.021203131167398505}
0.658 (+/-0.388) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.02128139045982712}
0.658 (+/-0.388) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.02135993860189796}
0.658 (+/-0.388) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.021438776659735086}
0.658 (+/-0.388) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.02151790570339755}
0.658 (+/-0.388) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.021597326806893944}
0.658 (+/-0.388) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 1.0, 'kernel': 'linear'}
0.674 (+/-0.375) for {'C': 10.0, 'kernel': 'linear'}
0.631 (+/-0.312) for {'C': 100.0, 'kernel': 'linear'}
0.637 (+/-0.307) for {'C': 1000.0, 'kernel': 'linear'}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'linear'}
0.620 (+/-0.321) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.617 (+/-0.238) for {'C': 3.9810717055349696, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.617 (+/-0.238) for {'C': 3.9810717055349696, 'kernel': 'rbf', 'gamma': 0.02097007578544648}
0.617 (+/-0.238) for {'C': 3.9810717055349696, 'kernel': 'rbf', 'gamma': 0.02104747488655977}
0.617 (+/-0.238) for {'C': 3.9810717055349696, 'kernel': 'rbf', 'gamma': 0.021125159662408542}
0.617 (+/-0.238) for {'C': 3.9810717055349696, 'kernel': 'rbf', 'gamma': 0.021203131167398505}
0.617 (+/-0.238) for {'C': 3.9810717055349696, 'kernel': 'rbf', 'gamma': 0.02128139045982712}
0.617 (+/-0.238) for {'C': 3.9810717055349696, 'kernel': 'rbf', 'gamma': 0.02135993860189796}
0.617 (+/-0.238) for {'C': 3.9810717055349696, 'kernel': 'rbf', 'gamma': 0.021438776659735086}
0.617 (+/-0.238) for {'C': 3.9810717055349696, 'kernel': 'rbf', 'gamma': 0.02151790570339755}
0.617 (+/-0.238) for {'C': 3.9810717055349696, 'kernel': 'rbf', 'gamma': 0.021597326806893944}
0.617 (+/-0.238) for {'C': 3.9810717055349696, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.617 (+/-0.238) for {'C': 6.309573444801927, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.617 (+/-0.238) for {'C': 6.309573444801927, 'kernel': 'rbf', 'gamma': 0.02097007578544648}
0.617 (+/-0.238) for {'C': 6.309573444801927, 'kernel': 'rbf', 'gamma': 0.02104747488655977}
0.617 (+/-0.238) for {'C': 6.309573444801927, 'kernel': 'rbf', 'gamma': 0.021125159662408542}
0.617 (+/-0.238) for {'C': 6.309573444801927, 'kernel': 'rbf', 'gamma': 0.021203131167398505}
0.617 (+/-0.238) for {'C': 6.309573444801927, 'kernel': 'rbf', 'gamma': 0.02128139045982712}
0.617 (+/-0.238) for {'C': 6.309573444801927, 'kernel': 'rbf', 'gamma': 0.02135993860189796}
0.617 (+/-0.238) for {'C': 6.309573444801927, 'kernel': 'rbf', 'gamma': 0.021438776659735086}
0.617 (+/-0.238) for {'C': 6.309573444801927, 'kernel': 'rbf', 'gamma': 0.02151790570339755}
0.617 (+/-0.238) for {'C': 6.309573444801927, 'kernel': 'rbf', 'gamma': 0.021597326806893944}
0.617 (+/-0.238) for {'C': 6.309573444801927, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.642 (+/-0.236) for {'C': 9.99999999999999, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.642 (+/-0.236) for {'C': 9.99999999999999, 'kernel': 'rbf', 'gamma': 0.02097007578544648}
0.642 (+/-0.236) for {'C': 9.99999999999999, 'kernel': 'rbf', 'gamma': 0.02104747488655977}
0.642 (+/-0.236) for {'C': 9.99999999999999, 'kernel': 'rbf', 'gamma': 0.021125159662408542}
0.642 (+/-0.236) for {'C': 9.99999999999999, 'kernel': 'rbf', 'gamma': 0.021203131167398505}
0.642 (+/-0.236) for {'C': 9.99999999999999, 'kernel': 'rbf', 'gamma': 0.02128139045982712}
0.642 (+/-0.236) for {'C': 9.99999999999999, 'kernel': 'rbf', 'gamma': 0.02135993860189796}
0.642 (+/-0.236) for {'C': 9.99999999999999, 'kernel': 'rbf', 'gamma': 0.021438776659735086}
0.642 (+/-0.236) for {'C': 9.99999999999999, 'kernel': 'rbf', 'gamma': 0.02151790570339755}
0.642 (+/-0.236) for {'C': 9.99999999999999, 'kernel': 'rbf', 'gamma': 0.021597326806893944}
0.642 (+/-0.236) for {'C': 9.99999999999999, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.666 (+/-0.316) for {'C': 15.84893192461112, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.666 (+/-0.316) for {'C': 15.84893192461112, 'kernel': 'rbf', 'gamma': 0.02097007578544648}
0.666 (+/-0.316) for {'C': 15.84893192461112, 'kernel': 'rbf', 'gamma': 0.02104747488655977}
0.666 (+/-0.316) for {'C': 15.84893192461112, 'kernel': 'rbf', 'gamma': 0.021125159662408542}
0.666 (+/-0.316) for {'C': 15.84893192461112, 'kernel': 'rbf', 'gamma': 0.021203131167398505}
0.666 (+/-0.316) for {'C': 15.84893192461112, 'kernel': 'rbf', 'gamma': 0.02128139045982712}
0.666 (+/-0.316) for {'C': 15.84893192461112, 'kernel': 'rbf', 'gamma': 0.02135993860189796}
0.666 (+/-0.316) for {'C': 15.84893192461112, 'kernel': 'rbf', 'gamma': 0.021438776659735086}
0.666 (+/-0.316) for {'C': 15.84893192461112, 'kernel': 'rbf', 'gamma': 0.02151790570339755}
0.666 (+/-0.316) for {'C': 15.84893192461112, 'kernel': 'rbf', 'gamma': 0.021597326806893944}
0.666 (+/-0.316) for {'C': 15.84893192461112, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.666 (+/-0.316) for {'C': 25.11886431509578, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.666 (+/-0.316) for {'C': 25.11886431509578, 'kernel': 'rbf', 'gamma': 0.02097007578544648}
0.666 (+/-0.316) for {'C': 25.11886431509578, 'kernel': 'rbf', 'gamma': 0.02104747488655977}
0.666 (+/-0.316) for {'C': 25.11886431509578, 'kernel': 'rbf', 'gamma': 0.021125159662408542}
0.666 (+/-0.316) for {'C': 25.11886431509578, 'kernel': 'rbf', 'gamma': 0.021203131167398505}
0.666 (+/-0.316) for {'C': 25.11886431509578, 'kernel': 'rbf', 'gamma': 0.02128139045982712}
0.666 (+/-0.316) for {'C': 25.11886431509578, 'kernel': 'rbf', 'gamma': 0.02135993860189796}
0.666 (+/-0.316) for {'C': 25.11886431509578, 'kernel': 'rbf', 'gamma': 0.021438776659735086}
0.666 (+/-0.316) for {'C': 25.11886431509578, 'kernel': 'rbf', 'gamma': 0.02151790570339755}
0.666 (+/-0.316) for {'C': 25.11886431509578, 'kernel': 'rbf', 'gamma': 0.021597326806893944}
0.666 (+/-0.316) for {'C': 25.11886431509578, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.682 (+/-0.294) for {'C': 39.810717055349684, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.682 (+/-0.294) for {'C': 39.810717055349684, 'kernel': 'rbf', 'gamma': 0.02097007578544648}
0.682 (+/-0.294) for {'C': 39.810717055349684, 'kernel': 'rbf', 'gamma': 0.02104747488655977}
0.682 (+/-0.294) for {'C': 39.810717055349684, 'kernel': 'rbf', 'gamma': 0.021125159662408542}
0.682 (+/-0.295) for {'C': 39.810717055349684, 'kernel': 'rbf', 'gamma': 0.021203131167398505}
0.682 (+/-0.295) for {'C': 39.810717055349684, 'kernel': 'rbf', 'gamma': 0.02128139045982712}
0.682 (+/-0.295) for {'C': 39.810717055349684, 'kernel': 'rbf', 'gamma': 0.02135993860189796}
0.682 (+/-0.295) for {'C': 39.810717055349684, 'kernel': 'rbf', 'gamma': 0.021438776659735086}
0.682 (+/-0.295) for {'C': 39.810717055349684, 'kernel': 'rbf', 'gamma': 0.02151790570339755}
0.682 (+/-0.295) for {'C': 39.810717055349684, 'kernel': 'rbf', 'gamma': 0.021597326806893944}
0.682 (+/-0.295) for {'C': 39.810717055349684, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.666 (+/-0.315) for {'C': 63.09573444801924, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.666 (+/-0.315) for {'C': 63.09573444801924, 'kernel': 'rbf', 'gamma': 0.02097007578544648}
0.666 (+/-0.315) for {'C': 63.09573444801924, 'kernel': 'rbf', 'gamma': 0.02104747488655977}
0.666 (+/-0.315) for {'C': 63.09573444801924, 'kernel': 'rbf', 'gamma': 0.021125159662408542}
0.666 (+/-0.315) for {'C': 63.09573444801924, 'kernel': 'rbf', 'gamma': 0.021203131167398505}
0.666 (+/-0.315) for {'C': 63.09573444801924, 'kernel': 'rbf', 'gamma': 0.02128139045982712}
0.666 (+/-0.315) for {'C': 63.09573444801924, 'kernel': 'rbf', 'gamma': 0.02135993860189796}
0.666 (+/-0.315) for {'C': 63.09573444801924, 'kernel': 'rbf', 'gamma': 0.021438776659735086}
0.666 (+/-0.315) for {'C': 63.09573444801924, 'kernel': 'rbf', 'gamma': 0.02151790570339755}
0.666 (+/-0.315) for {'C': 63.09573444801924, 'kernel': 'rbf', 'gamma': 0.021597326806893944}
0.666 (+/-0.315) for {'C': 63.09573444801924, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.665 (+/-0.315) for {'C': 99.99999999999991, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.665 (+/-0.315) for {'C': 99.99999999999991, 'kernel': 'rbf', 'gamma': 0.02097007578544648}
0.665 (+/-0.315) for {'C': 99.99999999999991, 'kernel': 'rbf', 'gamma': 0.02104747488655977}
0.665 (+/-0.315) for {'C': 99.99999999999991, 'kernel': 'rbf', 'gamma': 0.021125159662408542}
0.665 (+/-0.315) for {'C': 99.99999999999991, 'kernel': 'rbf', 'gamma': 0.021203131167398505}
0.665 (+/-0.315) for {'C': 99.99999999999991, 'kernel': 'rbf', 'gamma': 0.02128139045982712}
0.665 (+/-0.315) for {'C': 99.99999999999991, 'kernel': 'rbf', 'gamma': 0.02135993860189796}
0.665 (+/-0.315) for {'C': 99.99999999999991, 'kernel': 'rbf', 'gamma': 0.021438776659735086}
0.665 (+/-0.315) for {'C': 99.99999999999991, 'kernel': 'rbf', 'gamma': 0.02151790570339755}
0.665 (+/-0.315) for {'C': 99.99999999999991, 'kernel': 'rbf', 'gamma': 0.021597326806893944}
0.665 (+/-0.315) for {'C': 99.99999999999991, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.665 (+/-0.315) for {'C': 158.4893192461112, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.665 (+/-0.315) for {'C': 158.4893192461112, 'kernel': 'rbf', 'gamma': 0.02097007578544648}
0.665 (+/-0.315) for {'C': 158.4893192461112, 'kernel': 'rbf', 'gamma': 0.02104747488655977}
0.665 (+/-0.315) for {'C': 158.4893192461112, 'kernel': 'rbf', 'gamma': 0.021125159662408542}
0.665 (+/-0.315) for {'C': 158.4893192461112, 'kernel': 'rbf', 'gamma': 0.021203131167398505}
0.665 (+/-0.315) for {'C': 158.4893192461112, 'kernel': 'rbf', 'gamma': 0.02128139045982712}
0.665 (+/-0.315) for {'C': 158.4893192461112, 'kernel': 'rbf', 'gamma': 0.02135993860189796}
0.665 (+/-0.315) for {'C': 158.4893192461112, 'kernel': 'rbf', 'gamma': 0.021438776659735086}
0.665 (+/-0.315) for {'C': 158.4893192461112, 'kernel': 'rbf', 'gamma': 0.02151790570339755}
0.665 (+/-0.315) for {'C': 158.4893192461112, 'kernel': 'rbf', 'gamma': 0.021597326806893944}
0.665 (+/-0.315) for {'C': 158.4893192461112, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.663 (+/-0.315) for {'C': 251.1886431509577, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.663 (+/-0.315) for {'C': 251.1886431509577, 'kernel': 'rbf', 'gamma': 0.02097007578544648}
0.663 (+/-0.315) for {'C': 251.1886431509577, 'kernel': 'rbf', 'gamma': 0.02104747488655977}
0.663 (+/-0.315) for {'C': 251.1886431509577, 'kernel': 'rbf', 'gamma': 0.021125159662408542}
0.663 (+/-0.315) for {'C': 251.1886431509577, 'kernel': 'rbf', 'gamma': 0.021203131167398505}
0.662 (+/-0.315) for {'C': 251.1886431509577, 'kernel': 'rbf', 'gamma': 0.02128139045982712}
0.662 (+/-0.315) for {'C': 251.1886431509577, 'kernel': 'rbf', 'gamma': 0.02135993860189796}
0.662 (+/-0.314) for {'C': 251.1886431509577, 'kernel': 'rbf', 'gamma': 0.021438776659735086}
0.662 (+/-0.314) for {'C': 251.1886431509577, 'kernel': 'rbf', 'gamma': 0.02151790570339755}
0.662 (+/-0.314) for {'C': 251.1886431509577, 'kernel': 'rbf', 'gamma': 0.021597326806893944}
0.662 (+/-0.314) for {'C': 251.1886431509577, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.658 (+/-0.314) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.020892961308540407}
0.658 (+/-0.314) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.02097007578544648}
0.658 (+/-0.314) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.02104747488655977}
0.658 (+/-0.314) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.021125159662408542}
0.658 (+/-0.314) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.021203131167398505}
0.658 (+/-0.314) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.02128139045982712}
0.658 (+/-0.314) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.02135993860189796}
0.658 (+/-0.314) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.021438776659735086}
0.658 (+/-0.314) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.02151790570339755}
0.658 (+/-0.314) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.021597326806893944}
0.658 (+/-0.314) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.021677041048196954}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.663 (+/-0.316) for {'C': 10.0, 'kernel': 'linear'}
0.661 (+/-0.315) for {'C': 100.0, 'kernel': 'linear'}
0.637 (+/-0.239) for {'C': 1000.0, 'kernel': 'linear'}
0.585 (+/-0.236) for {'C': 10000.0, 'kernel': 'linear'}
0.611 (+/-0.242) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.17 0.17 0.17 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 39.810717055349684, 'kernel': 'rbf', 'gamma': 0.021203131167398505}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6822115384615385
第2轮loop中,tunemodel函数得到的最优参数是: ([{'C': array([25.11886432, 27.54228703, 30.1995172 , 33.11311215, 36.30780548,
39.81071706, 43.65158322, 47.86300923, 52.48074602, 57.54399373,
63.09573445]), 'kernel': ['rbf'], 'gamma': array([0.02112516, 0.02114073, 0.02115631, 0.02117191, 0.02118751,
0.02120313, 0.02121876, 0.0212344 , 0.02125005, 0.02126572,
0.02128139])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}], {'C': 39.810717055349684, 'kernel': 'rbf', 'gamma': 0.021203131167398505}, 0.6822115384615385)
这是第2次迭代微调C和gamma。
第2次迭代,得到delta: [7.10542736e-15 7.82592924e-05]
SVC模型clf_op_iter的参数是: {'kernel': 'rbf', 'decision_function_shape': 'ovr', 'class_weight': {-1: 15}, 'tol': 0.001, 'gamma': 0.021203131167398505, 'random_state': 0, 'cache_size': 200, 'verbose': False, 'degree': 3, 'C': 39.810717055349684, 'probability': False, 'shrinking': True, 'coef0': 0.0, 'max_iter': -1}
训练模型SVC对训练样本的预测准确率: 0.9468462083628633
测试集中,预测为舞弊样本的有: (array([ 8, 10, 11, 12, 13, 14, 17, 18, 19, 20, 21,
22, 23, 24, 25, 30, 31, 32, 33, 34, 35, 36,
37, 38, 39, 40, 41, 42, 43, 44, 46, 47, 48,
49, 50, 51, 52, 53, 54, 57, 58, 59, 60, 63,
64, 65, 66, 67, 68, 71, 72, 73, 74, 75, 76,
77, 78, 79, 84, 87, 88, 90, 91, 92, 93, 98,
101, 102, 105, 106, 107, 108, 109, 110, 111, 112, 113,
114, 115, 116, 117, 118, 119, 120, 121, 127, 128, 129,
135, 136, 141, 142, 143, 144, 145, 146, 150, 151, 153,
154, 155, 156, 157, 158, 162, 163, 164, 165, 166, 169,
176, 177, 180, 181, 182, 183, 184, 185, 186, 187, 192,
193, 195, 196, 197, 199, 200, 201, 202, 204, 205, 207,
208, 209, 212, 213, 214, 215, 216, 217, 218, 219, 225,
229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239,
242, 243, 244, 245, 246, 247, 251, 252, 253, 254, 255,
256, 257, 258, 259, 260, 261, 262, 264, 267, 268, 269,
270, 274, 275, 276, 277, 278, 279, 280, 281, 283, 284,
285, 286, 287, 288, 290, 295, 296, 298, 299, 300, 301,
302, 306, 307, 308, 313, 316, 317, 318, 319, 320, 321,
322, 323, 324, 325, 330, 331, 332, 333, 334, 338, 339,
340, 344, 346, 347, 348, 349, 351, 352, 353, 354, 356,
358, 359, 360, 361, 362, 363, 364, 365, 366, 367, 368,
369, 370, 374, 375, 379, 383, 384, 385, 386, 388, 391,
392, 393, 394, 395, 396, 398, 399, 403, 404, 410, 413,
414, 419, 420, 421, 422, 423, 426, 427, 428, 429, 430,
431, 432, 433, 434, 438, 439, 440, 441, 442, 443, 444,
446, 447, 448, 449, 450, 454, 455, 456, 457, 460, 461,
463, 465, 466, 467, 468, 469, 471, 472, 473, 474, 475,
476, 477, 478, 479, 480, 481, 482, 483, 484, 485, 486,
487, 488, 489, 490, 491, 492, 493, 496, 498, 499, 500,
501, 502, 503, 508, 509, 511, 512, 513, 514, 515, 516,
517, 518, 519, 520, 521, 529, 530, 532, 534, 535, 540,
541, 542, 543, 544, 545, 546, 547, 549, 551, 552, 555,
557, 558, 560, 561, 562, 563, 564, 565, 566, 567, 568,
570, 571, 572, 573, 578, 582, 583, 584, 586, 587, 589,
590, 592, 593, 594, 595, 599, 600, 601, 604, 605, 611,
612, 613, 614, 615, 616, 617, 618, 619, 620, 621, 622,
623, 624, 629, 630, 631, 633, 634, 635, 641, 642, 643,
646, 651, 653, 655, 656, 657, 658, 659, 660, 661, 662,
663, 664, 665, 666, 667, 668, 669, 673, 674, 675, 676,
677, 680, 681, 682, 683, 684, 685, 686, 687, 688, 691,
692, 693, 694, 695, 696, 697, 698, 699, 701, 702, 703,
705, 706, 713, 714, 715, 716, 717, 718, 720, 721, 722,
723, 724, 729, 732, 733, 734, 735, 736, 742, 743, 751,
752, 753, 754, 756, 758, 759, 760, 761, 769, 774, 775,
776, 777, 778, 785, 786, 787, 788, 789, 790, 791, 792,
793, 794, 795, 796, 797, 806, 807, 812, 813, 814, 815,
816, 817, 819, 820, 824, 825, 826, 827, 828, 829, 830,
840, 841, 843, 844, 845, 846, 847, 848, 849, 850, 851,
852, 853, 854, 855, 856, 857, 859, 862, 864, 872, 874,
875, 876, 877, 878, 879, 880, 881, 882, 883, 884, 885,
893, 897, 898, 901, 902, 903, 904, 905, 906, 907, 910,
911, 912, 913, 914, 916, 919, 922, 923, 924, 925, 927,
928, 929, 930, 931, 932, 933, 934, 935, 946, 947, 948,
949, 950, 951, 952, 953, 954, 955, 956, 959, 960, 961,
963, 964, 965, 966, 967, 969, 970, 971, 976, 977, 978,
979, 980, 981, 982, 983, 984, 985, 992, 993, 995, 998,
1000, 1002, 1003, 1004, 1005, 1006, 1007, 1008, 1009, 1011, 1012,
1013, 1014, 1015, 1016, 1017, 1020, 1021, 1022, 1030, 1032, 1033,
1034, 1035, 1036, 1037, 1039, 1040, 1041, 1047, 1048, 1049, 1050,
1051, 1055, 1056, 1057, 1060, 1061, 1064, 1065, 1067, 1068, 1071,
1073, 1074, 1080, 1081, 1082, 1085, 1086, 1089, 1090, 1091, 1092,
1093, 1094, 1095, 1096, 1097, 1098, 1099, 1100, 1101, 1105, 1106,
1107, 1112, 1113, 1114, 1117, 1119, 1121, 1122, 1123, 1124, 1125,
1127, 1129, 1131, 1132, 1134, 1135, 1137, 1138, 1140, 1141, 1142,
1144, 1148, 1152, 1154, 1156, 1157, 1158, 1160, 1163, 1164, 1166,
1167, 1168, 1170, 1171, 1175, 1180, 1181, 1183, 1188, 1191, 1192,
1196, 1200, 1205, 1208, 1211, 1214, 1215, 1216, 1217, 1218, 1219,
1222, 1226, 1227, 1230, 1231, 1232, 1233, 1234, 1235, 1236, 1237,
1238, 1239, 1240, 1241, 1242, 1243, 1244, 1245, 1246, 1247, 1248,
1249, 1250, 1251, 1252, 1254, 1255, 1256], dtype=int64),)
测试集中,实际为舞弊样本的有: (array([1246, 1247, 1248, 1249, 1250, 1251, 1252, 1253, 1254, 1255, 1256],
dtype=int64),)
预测的舞弊样本数目: 777
训练模型SVC对测试样本的预测准确率: 0.4457831325301205
以上是第31次特征筛选。
第31次特征筛选,AUC值是: 0.6467605428279586
X_train_iter_svc.shape is: (1257, 21)
X_test_iter_svc.shape is: (1257, 21)
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.639 (+/-0.396) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.748 (+/-0.449) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.20 0.17 0.18 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.589 (+/-0.231) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.666 (+/-0.316) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.20 0.17 0.18 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.99 0.99 629
本轮grid search结果,得到最好的参数选择是: {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6663461538461538
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.748 (+/-0.449) for {'C': 1.0, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.632 (+/-0.309) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.20 0.17 0.18 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.637 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.20 0.17 0.18 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.99 0.99 629
本轮grid search结果,得到最好的参数选择是: {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6663461538461538
RBF的粗grid search最佳超参: {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001} RBF的粗grid search最高分值: 0.6663461538461538
linear的粗grid search最佳超参: {'C': 1.0, 'kernel': 'linear'} linear的粗grid search最高分值: 0.6663461538461538
粗grid search得到的parameter是:
[{'C': array([1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02, 1.e+03, 1.e+04, 1.e+05,
1.e+06, 1.e+07, 1.e+08]), 'kernel': ['rbf'], 'gamma': array([1.e-08, 1.e-07, 1.e-06, 1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01,
1.e+00, 1.e+01, 1.e+02])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.326) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.748 (+/-0.449) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.639 (+/-0.326) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.646 (+/-0.388) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.748 (+/-0.449) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.664 (+/-0.317) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.396) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.748 (+/-0.449) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.664 (+/-0.317) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.627 (+/-0.313) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.396) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.747 (+/-0.502) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.748 (+/-0.449) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.673 (+/-0.330) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.633 (+/-0.312) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.396) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.797 (+/-0.492) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.747 (+/-0.449) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.693 (+/-0.374) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.635 (+/-0.312) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.631 (+/-0.310) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.396) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.649 (+/-0.778) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.597 (+/-0.401) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.698 (+/-0.352) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.629 (+/-0.312) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.614 (+/-0.327) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.614 (+/-0.327) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.396) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.649 (+/-0.778) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.597 (+/-0.401) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.677 (+/-0.374) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.625 (+/-0.314) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.614 (+/-0.327) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.614 (+/-0.327) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.396) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'linear'}
0.706 (+/-0.383) for {'C': 10.0, 'kernel': 'linear'}
0.635 (+/-0.313) for {'C': 100.0, 'kernel': 'linear'}
0.632 (+/-0.309) for {'C': 1000.0, 'kernel': 'linear'}
0.629 (+/-0.312) for {'C': 10000.0, 'kernel': 'linear'}
0.624 (+/-0.319) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [1]
检查交叉验证集中测试集标签是否有预测不到的值: {-1}
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 1.00 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.006) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.004) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.666 (+/-0.316) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.239) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.004) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.666 (+/-0.316) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.665 (+/-0.315) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.587 (+/-0.234) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.589 (+/-0.231) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.004) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.666 (+/-0.316) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.665 (+/-0.315) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.636 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.588 (+/-0.232) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.589 (+/-0.231) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.004) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.617 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.666 (+/-0.316) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.665 (+/-0.316) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.661 (+/-0.316) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.588 (+/-0.233) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.588 (+/-0.233) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.589 (+/-0.231) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.004) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.642 (+/-0.236) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.641 (+/-0.236) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.665 (+/-0.316) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.661 (+/-0.316) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.637 (+/-0.238) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.586 (+/-0.236) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.588 (+/-0.233) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.589 (+/-0.231) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.004) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.617 (+/-0.238) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.542 (+/-0.171) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.682 (+/-0.296) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.660 (+/-0.317) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.587 (+/-0.234) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.586 (+/-0.236) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.586 (+/-0.236) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.588 (+/-0.233) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.589 (+/-0.231) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.004) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.614 (+/-0.244) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.542 (+/-0.171) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.664 (+/-0.318) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.659 (+/-0.311) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.587 (+/-0.234) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.586 (+/-0.236) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.586 (+/-0.236) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.588 (+/-0.233) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.589 (+/-0.231) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.004) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 10.0, 'kernel': 'linear'}
0.636 (+/-0.237) for {'C': 100.0, 'kernel': 'linear'}
0.637 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
0.636 (+/-0.240) for {'C': 10000.0, 'kernel': 'linear'}
0.610 (+/-0.245) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.17 0.17 0.17 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.681729738642922
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.656 (+/-0.384) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.639 (+/-0.306) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.619 (+/-0.322) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.637 (+/-0.307) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.323) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.646 (+/-0.388) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.662 (+/-0.298) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.624 (+/-0.316) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.396) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.662 (+/-0.298) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.624 (+/-0.316) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.627 (+/-0.313) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.396) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.747 (+/-0.502) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.797 (+/-0.492) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.662 (+/-0.373) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.624 (+/-0.316) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.633 (+/-0.312) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.396) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.797 (+/-0.492) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.654 (+/-0.293) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.656 (+/-0.389) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.635 (+/-0.312) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.631 (+/-0.310) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.396) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.699 (+/-0.797) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.599 (+/-0.399) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.636 (+/-0.292) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.626 (+/-0.314) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.614 (+/-0.327) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.614 (+/-0.327) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.396) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.599 (+/-0.745) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.597 (+/-0.400) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.611 (+/-0.305) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.625 (+/-0.314) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.614 (+/-0.327) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.614 (+/-0.327) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.396) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 1.0, 'kernel': 'linear'}
0.666 (+/-0.379) for {'C': 10.0, 'kernel': 'linear'}
0.629 (+/-0.313) for {'C': 100.0, 'kernel': 'linear'}
0.632 (+/-0.309) for {'C': 1000.0, 'kernel': 'linear'}
0.629 (+/-0.312) for {'C': 10000.0, 'kernel': 'linear'}
0.624 (+/-0.319) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.006) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.665 (+/-0.314) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.611 (+/-0.241) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.004) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.665 (+/-0.314) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.608 (+/-0.244) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.239) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.004) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.681 (+/-0.294) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.657 (+/-0.314) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.587 (+/-0.234) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.589 (+/-0.231) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.004) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.681 (+/-0.294) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.657 (+/-0.314) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.636 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.588 (+/-0.232) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.589 (+/-0.231) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.004) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.617 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.642 (+/-0.236) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.665 (+/-0.314) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.658 (+/-0.313) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.661 (+/-0.316) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.588 (+/-0.233) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.588 (+/-0.233) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.589 (+/-0.231) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.004) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.642 (+/-0.236) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.681 (+/-0.294) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.657 (+/-0.313) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.661 (+/-0.316) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.637 (+/-0.238) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.586 (+/-0.236) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.588 (+/-0.233) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.589 (+/-0.231) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.004) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.642 (+/-0.236) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.562 (+/-0.195) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.677 (+/-0.294) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.658 (+/-0.314) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.587 (+/-0.234) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.586 (+/-0.236) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.586 (+/-0.236) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.588 (+/-0.233) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.589 (+/-0.231) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.004) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.575 (+/-0.274) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.552 (+/-0.173) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.658 (+/-0.312) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.659 (+/-0.312) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.587 (+/-0.234) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.586 (+/-0.236) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.586 (+/-0.236) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.588 (+/-0.233) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.589 (+/-0.231) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.004) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.662 (+/-0.314) for {'C': 10.0, 'kernel': 'linear'}
0.660 (+/-0.315) for {'C': 100.0, 'kernel': 'linear'}
0.637 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
0.636 (+/-0.240) for {'C': 10000.0, 'kernel': 'linear'}
0.610 (+/-0.245) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.14 0.17 0.15 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6812494847060763
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.797 (+/-0.492) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.797 (+/-0.492) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.797 (+/-0.492) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.747 (+/-0.502) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.798 (+/-0.492) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.747 (+/-0.449) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.748 (+/-0.449) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.663 (+/-0.368) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.681 (+/-0.373) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.681 (+/-0.373) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.693 (+/-0.374) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.797 (+/-0.492) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.797 (+/-0.492) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.798 (+/-0.492) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.747 (+/-0.502) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.748 (+/-0.449) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.748 (+/-0.449) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.681 (+/-0.372) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.651 (+/-0.308) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.681 (+/-0.373) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.677 (+/-0.374) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.648 (+/-0.306) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.797 (+/-0.492) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.797 (+/-0.492) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.798 (+/-0.492) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.747 (+/-0.502) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.798 (+/-0.437) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.748 (+/-0.449) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.631 (+/-0.298) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.668 (+/-0.318) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.681 (+/-0.373) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.658 (+/-0.329) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.644 (+/-0.309) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.797 (+/-0.492) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.797 (+/-0.492) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.798 (+/-0.492) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.697 (+/-0.437) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.706 (+/-0.360) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.764 (+/-0.422) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.652 (+/-0.313) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.677 (+/-0.374) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.670 (+/-0.381) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.652 (+/-0.335) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.645 (+/-0.318) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.597 (+/-0.401) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.798 (+/-0.492) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.798 (+/-0.492) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.697 (+/-0.437) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.698 (+/-0.409) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.731 (+/-0.394) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.652 (+/-0.313) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.660 (+/-0.374) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.668 (+/-0.382) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.642 (+/-0.330) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.629 (+/-0.313) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.647 (+/-0.459) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.797 (+/-0.492) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.697 (+/-0.492) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.697 (+/-0.437) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.681 (+/-0.427) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.698 (+/-0.352) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.652 (+/-0.313) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.665 (+/-0.381) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.668 (+/-0.383) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.654 (+/-0.381) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.629 (+/-0.312) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.647 (+/-0.459) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.798 (+/-0.492) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.697 (+/-0.492) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.656 (+/-0.384) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.681 (+/-0.427) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.681 (+/-0.373) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.642 (+/-0.307) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.656 (+/-0.377) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.651 (+/-0.336) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.654 (+/-0.381) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.626 (+/-0.313) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.647 (+/-0.459) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.798 (+/-0.492) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.672 (+/-0.452) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.656 (+/-0.384) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.681 (+/-0.427) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.677 (+/-0.374) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.642 (+/-0.307) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.656 (+/-0.377) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.650 (+/-0.337) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.654 (+/-0.381) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.626 (+/-0.313) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.597 (+/-0.401) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.798 (+/-0.492) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.672 (+/-0.452) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.656 (+/-0.384) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.662 (+/-0.372) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.681 (+/-0.373) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.642 (+/-0.307) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.621 (+/-0.302) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.637 (+/-0.317) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.654 (+/-0.381) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.625 (+/-0.314) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.597 (+/-0.401) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.798 (+/-0.492) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.672 (+/-0.452) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.656 (+/-0.384) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.662 (+/-0.372) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.677 (+/-0.374) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.621 (+/-0.317) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.622 (+/-0.302) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.637 (+/-0.317) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.655 (+/-0.381) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.625 (+/-0.314) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.647 (+/-0.459) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.798 (+/-0.492) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.672 (+/-0.452) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.656 (+/-0.384) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.662 (+/-0.372) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.677 (+/-0.374) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.621 (+/-0.317) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.621 (+/-0.302) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.633 (+/-0.315) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.655 (+/-0.381) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.625 (+/-0.314) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'linear'}
0.706 (+/-0.383) for {'C': 10.0, 'kernel': 'linear'}
0.635 (+/-0.313) for {'C': 100.0, 'kernel': 'linear'}
0.632 (+/-0.309) for {'C': 1000.0, 'kernel': 'linear'}
0.629 (+/-0.312) for {'C': 10000.0, 'kernel': 'linear'}
0.624 (+/-0.319) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.25 0.17 0.20 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.642 (+/-0.236) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.642 (+/-0.236) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.642 (+/-0.236) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.617 (+/-0.238) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.667 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.641 (+/-0.236) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.666 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.665 (+/-0.313) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.665 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.665 (+/-0.317) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.665 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.642 (+/-0.236) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.642 (+/-0.236) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.667 (+/-0.316) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.617 (+/-0.238) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.666 (+/-0.316) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.666 (+/-0.316) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.665 (+/-0.315) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.665 (+/-0.315) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.665 (+/-0.316) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.665 (+/-0.316) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.663 (+/-0.316) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.642 (+/-0.236) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.642 (+/-0.236) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.667 (+/-0.316) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.617 (+/-0.238) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.683 (+/-0.296) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.666 (+/-0.316) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.664 (+/-0.312) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.665 (+/-0.315) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.665 (+/-0.317) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.664 (+/-0.317) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.662 (+/-0.318) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.642 (+/-0.236) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.642 (+/-0.236) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.667 (+/-0.316) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.616 (+/-0.237) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.682 (+/-0.295) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.683 (+/-0.296) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.664 (+/-0.313) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.664 (+/-0.313) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.663 (+/-0.317) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.662 (+/-0.318) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.661 (+/-0.320) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.542 (+/-0.171) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.667 (+/-0.316) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.667 (+/-0.316) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.616 (+/-0.237) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.681 (+/-0.295) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.682 (+/-0.296) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.664 (+/-0.314) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.664 (+/-0.312) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.663 (+/-0.317) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.661 (+/-0.318) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.659 (+/-0.317) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.567 (+/-0.208) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.642 (+/-0.236) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.617 (+/-0.327) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.616 (+/-0.238) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.656 (+/-0.311) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.682 (+/-0.296) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.663 (+/-0.315) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.663 (+/-0.311) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.661 (+/-0.319) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.660 (+/-0.319) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.660 (+/-0.317) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.567 (+/-0.208) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.667 (+/-0.316) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.617 (+/-0.327) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.616 (+/-0.237) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.656 (+/-0.311) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.665 (+/-0.317) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.663 (+/-0.315) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.663 (+/-0.311) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.661 (+/-0.318) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.659 (+/-0.320) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.659 (+/-0.315) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.567 (+/-0.208) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.667 (+/-0.316) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.617 (+/-0.326) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.615 (+/-0.237) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.656 (+/-0.311) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.665 (+/-0.317) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.663 (+/-0.314) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.663 (+/-0.312) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.660 (+/-0.319) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.659 (+/-0.320) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.660 (+/-0.312) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.542 (+/-0.171) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.667 (+/-0.316) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.617 (+/-0.326) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.615 (+/-0.238) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.673 (+/-0.327) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.665 (+/-0.318) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.663 (+/-0.315) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.662 (+/-0.312) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.659 (+/-0.318) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.659 (+/-0.320) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.659 (+/-0.311) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.542 (+/-0.171) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.667 (+/-0.316) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.617 (+/-0.326) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.615 (+/-0.238) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.672 (+/-0.328) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.664 (+/-0.318) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.637 (+/-0.324) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.662 (+/-0.313) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.659 (+/-0.318) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.660 (+/-0.320) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.660 (+/-0.311) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.567 (+/-0.208) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.667 (+/-0.316) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.617 (+/-0.326) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.615 (+/-0.238) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.672 (+/-0.328) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.664 (+/-0.318) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.637 (+/-0.324) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.662 (+/-0.312) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.659 (+/-0.318) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.660 (+/-0.320) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.659 (+/-0.311) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 10.0, 'kernel': 'linear'}
0.636 (+/-0.237) for {'C': 100.0, 'kernel': 'linear'}
0.637 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
0.636 (+/-0.240) for {'C': 10000.0, 'kernel': 'linear'}
0.610 (+/-0.245) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.25 0.17 0.20 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.99 629
本轮grid search结果,得到最好的参数选择是: {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6830128205128205
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.797 (+/-0.492) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.798 (+/-0.492) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.848 (+/-0.460) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.798 (+/-0.492) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.735 (+/-0.402) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.654 (+/-0.293) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.628 (+/-0.299) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.616 (+/-0.288) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.621 (+/-0.302) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.629 (+/-0.312) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.656 (+/-0.389) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.797 (+/-0.492) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.798 (+/-0.492) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.798 (+/-0.492) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.689 (+/-0.374) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.679 (+/-0.372) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.637 (+/-0.307) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.611 (+/-0.289) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.623 (+/-0.302) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.618 (+/-0.305) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.624 (+/-0.316) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.624 (+/-0.316) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.647 (+/-0.459) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.798 (+/-0.492) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.789 (+/-0.443) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.668 (+/-0.371) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.648 (+/-0.313) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.683 (+/-0.353) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.605 (+/-0.292) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.635 (+/-0.321) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.625 (+/-0.316) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.623 (+/-0.316) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.625 (+/-0.314) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.622 (+/-0.404) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.781 (+/-0.474) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.758 (+/-0.429) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.641 (+/-0.307) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.639 (+/-0.297) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.688 (+/-0.357) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.624 (+/-0.315) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.657 (+/-0.388) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.623 (+/-0.317) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.624 (+/-0.316) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.625 (+/-0.314) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.601 (+/-0.397) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.773 (+/-0.473) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.705 (+/-0.426) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.619 (+/-0.299) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.639 (+/-0.287) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.651 (+/-0.294) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.622 (+/-0.317) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.647 (+/-0.385) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.622 (+/-0.318) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.617 (+/-0.306) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.624 (+/-0.315) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.599 (+/-0.399) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.768 (+/-0.475) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.704 (+/-0.427) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.618 (+/-0.299) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.614 (+/-0.289) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.636 (+/-0.292) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.622 (+/-0.318) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.655 (+/-0.390) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.622 (+/-0.318) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.613 (+/-0.304) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.626 (+/-0.314) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.598 (+/-0.400) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.764 (+/-0.477) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.686 (+/-0.426) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.617 (+/-0.300) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.623 (+/-0.299) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.619 (+/-0.304) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.613 (+/-0.309) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.646 (+/-0.387) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.622 (+/-0.318) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.613 (+/-0.304) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.626 (+/-0.314) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.598 (+/-0.400) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.764 (+/-0.477) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.650 (+/-0.380) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.617 (+/-0.300) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.623 (+/-0.299) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.612 (+/-0.304) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.613 (+/-0.309) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.646 (+/-0.387) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.622 (+/-0.318) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.613 (+/-0.304) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.626 (+/-0.314) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.598 (+/-0.400) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.764 (+/-0.477) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.647 (+/-0.382) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.613 (+/-0.298) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.625 (+/-0.297) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.617 (+/-0.305) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.613 (+/-0.309) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.613 (+/-0.309) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.622 (+/-0.318) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.613 (+/-0.304) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.625 (+/-0.314) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.598 (+/-0.400) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.764 (+/-0.477) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.690 (+/-0.431) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.610 (+/-0.298) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.620 (+/-0.298) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.612 (+/-0.304) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.592 (+/-0.311) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.613 (+/-0.309) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.622 (+/-0.318) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.613 (+/-0.304) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.625 (+/-0.314) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.597 (+/-0.400) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.798 (+/-0.492) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.697 (+/-0.420) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.610 (+/-0.298) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.617 (+/-0.290) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.611 (+/-0.305) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.592 (+/-0.311) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.613 (+/-0.309) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.622 (+/-0.318) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.613 (+/-0.304) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.625 (+/-0.314) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 1.0, 'kernel': 'linear'}
0.666 (+/-0.379) for {'C': 10.0, 'kernel': 'linear'}
0.629 (+/-0.313) for {'C': 100.0, 'kernel': 'linear'}
0.632 (+/-0.309) for {'C': 1000.0, 'kernel': 'linear'}
0.629 (+/-0.312) for {'C': 10000.0, 'kernel': 'linear'}
0.624 (+/-0.319) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.642 (+/-0.236) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.667 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.683 (+/-0.296) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.667 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.682 (+/-0.295) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.681 (+/-0.294) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.663 (+/-0.311) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.663 (+/-0.311) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.661 (+/-0.313) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.660 (+/-0.317) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.657 (+/-0.313) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.642 (+/-0.236) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.667 (+/-0.316) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.667 (+/-0.316) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.666 (+/-0.314) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.681 (+/-0.295) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.664 (+/-0.315) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.662 (+/-0.310) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.661 (+/-0.316) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.658 (+/-0.316) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.656 (+/-0.317) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.657 (+/-0.313) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.567 (+/-0.208) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.667 (+/-0.316) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.683 (+/-0.296) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.665 (+/-0.311) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.680 (+/-0.295) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.680 (+/-0.294) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.659 (+/-0.311) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.659 (+/-0.316) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.656 (+/-0.317) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.656 (+/-0.315) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.658 (+/-0.314) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.567 (+/-0.208) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.667 (+/-0.316) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.682 (+/-0.295) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.664 (+/-0.311) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.680 (+/-0.295) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.679 (+/-0.295) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.658 (+/-0.313) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.656 (+/-0.316) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.655 (+/-0.317) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.656 (+/-0.315) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.658 (+/-0.314) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.565 (+/-0.202) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.666 (+/-0.315) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.657 (+/-0.310) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.663 (+/-0.309) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.679 (+/-0.296) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.678 (+/-0.295) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.656 (+/-0.311) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.654 (+/-0.318) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.654 (+/-0.314) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.657 (+/-0.317) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.658 (+/-0.313) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.563 (+/-0.198) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.666 (+/-0.314) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.657 (+/-0.310) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.663 (+/-0.311) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.654 (+/-0.311) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.677 (+/-0.294) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.654 (+/-0.314) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.652 (+/-0.320) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.653 (+/-0.316) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.657 (+/-0.315) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.658 (+/-0.314) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.559 (+/-0.187) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.666 (+/-0.314) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.656 (+/-0.310) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.662 (+/-0.311) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.653 (+/-0.314) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.660 (+/-0.314) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.652 (+/-0.316) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.651 (+/-0.317) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.654 (+/-0.316) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.657 (+/-0.316) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.658 (+/-0.315) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.557 (+/-0.182) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.666 (+/-0.314) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.655 (+/-0.309) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.662 (+/-0.311) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.653 (+/-0.314) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.658 (+/-0.312) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.652 (+/-0.317) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.651 (+/-0.317) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.654 (+/-0.314) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.657 (+/-0.314) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.659 (+/-0.312) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.555 (+/-0.177) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.666 (+/-0.314) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.655 (+/-0.309) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.662 (+/-0.312) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.669 (+/-0.332) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.658 (+/-0.313) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.652 (+/-0.317) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.652 (+/-0.315) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.654 (+/-0.314) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.658 (+/-0.313) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.659 (+/-0.312) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.554 (+/-0.175) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.666 (+/-0.314) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.664 (+/-0.315) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.662 (+/-0.311) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.669 (+/-0.331) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.658 (+/-0.311) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.626 (+/-0.323) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.652 (+/-0.315) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.654 (+/-0.313) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.658 (+/-0.314) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.659 (+/-0.311) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.553 (+/-0.173) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.667 (+/-0.316) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.680 (+/-0.295) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.662 (+/-0.311) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.669 (+/-0.331) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.658 (+/-0.312) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.626 (+/-0.323) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.652 (+/-0.316) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.654 (+/-0.313) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.658 (+/-0.314) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.659 (+/-0.312) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.662 (+/-0.314) for {'C': 10.0, 'kernel': 'linear'}
0.660 (+/-0.315) for {'C': 100.0, 'kernel': 'linear'}
0.637 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
0.636 (+/-0.240) for {'C': 10000.0, 'kernel': 'linear'}
0.610 (+/-0.245) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6833333333333332
发现最优参数C为原先的最大/最小值,直接重新设置超参。
循环迭代之前,delta is: [9.00000000e+06 7.48811357e-07]
执行tunemodel()函数前,使用的grid search parameter是:
[{'C': array([1.e+01, 1.e+02, 1.e+03, 1.e+04, 1.e+05, 1.e+06, 1.e+07, 1.e+08,
1.e+09, 1.e+10, 1.e+11]), 'kernel': ['rbf'], 'gamma': array([1.58489319e-07, 1.73780083e-07, 1.90546072e-07, 2.08929613e-07,
2.29086765e-07, 2.51188643e-07, 2.75422870e-07, 3.01995172e-07,
3.31131121e-07, 3.63078055e-07, 3.98107171e-07])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.747 (+/-0.502) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.747 (+/-0.502) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.747 (+/-0.502) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.747 (+/-0.502) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.747 (+/-0.502) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.747 (+/-0.502) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.747 (+/-0.502) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.747 (+/-0.502) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.747 (+/-0.502) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.747 (+/-0.502) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.747 (+/-0.502) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.797 (+/-0.492) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.747 (+/-0.502) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.797 (+/-0.492) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.798 (+/-0.492) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.797 (+/-0.492) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.797 (+/-0.492) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.798 (+/-0.492) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.797 (+/-0.492) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.797 (+/-0.492) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.797 (+/-0.492) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.747 (+/-0.502) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.797 (+/-0.492) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.747 (+/-0.502) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.747 (+/-0.502) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.647 (+/-0.460) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.697 (+/-0.492) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.697 (+/-0.492) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.672 (+/-0.399) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.681 (+/-0.427) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.744 (+/-0.433) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.748 (+/-0.449) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.697 (+/-0.437) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.798 (+/-0.492) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.747 (+/-0.502) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.697 (+/-0.438) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.647 (+/-0.460) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.722 (+/-0.473) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.672 (+/-0.452) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.653 (+/-0.401) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.671 (+/-0.433) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.739 (+/-0.437) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.714 (+/-0.418) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.656 (+/-0.384) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.798 (+/-0.492) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.747 (+/-0.502) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.697 (+/-0.438) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.647 (+/-0.460) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.722 (+/-0.473) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.672 (+/-0.452) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.653 (+/-0.401) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.671 (+/-0.433) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.739 (+/-0.437) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.714 (+/-0.418) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.631 (+/-0.319) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.798 (+/-0.492) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.747 (+/-0.502) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.697 (+/-0.438) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.647 (+/-0.460) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.697 (+/-0.492) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.672 (+/-0.452) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.653 (+/-0.401) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.671 (+/-0.433) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.739 (+/-0.437) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.714 (+/-0.418) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.631 (+/-0.319) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.798 (+/-0.492) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.747 (+/-0.502) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.697 (+/-0.438) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.647 (+/-0.460) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.722 (+/-0.473) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.672 (+/-0.452) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.653 (+/-0.401) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.671 (+/-0.433) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.739 (+/-0.437) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.714 (+/-0.418) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.631 (+/-0.319) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'linear'}
0.706 (+/-0.383) for {'C': 10.0, 'kernel': 'linear'}
0.635 (+/-0.313) for {'C': 100.0, 'kernel': 'linear'}
0.632 (+/-0.309) for {'C': 1000.0, 'kernel': 'linear'}
0.629 (+/-0.312) for {'C': 10000.0, 'kernel': 'linear'}
0.624 (+/-0.319) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.617 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.617 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.617 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.617 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.617 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.617 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.617 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.617 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.617 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.617 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.617 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.642 (+/-0.236) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.617 (+/-0.238) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.642 (+/-0.236) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.667 (+/-0.316) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.642 (+/-0.236) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.642 (+/-0.236) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.667 (+/-0.316) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.642 (+/-0.236) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.642 (+/-0.236) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.642 (+/-0.236) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.617 (+/-0.238) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.642 (+/-0.236) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.617 (+/-0.238) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.642 (+/-0.325) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.592 (+/-0.320) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.617 (+/-0.327) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.617 (+/-0.327) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.641 (+/-0.324) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.665 (+/-0.315) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.682 (+/-0.296) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.666 (+/-0.316) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.616 (+/-0.238) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.667 (+/-0.316) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.617 (+/-0.238) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.641 (+/-0.325) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.592 (+/-0.320) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.633 (+/-0.318) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.617 (+/-0.326) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.640 (+/-0.323) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.664 (+/-0.314) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.682 (+/-0.296) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.666 (+/-0.316) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.615 (+/-0.238) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.667 (+/-0.316) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.617 (+/-0.238) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.641 (+/-0.325) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.592 (+/-0.320) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.633 (+/-0.318) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.617 (+/-0.326) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.640 (+/-0.323) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.664 (+/-0.314) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.682 (+/-0.296) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.666 (+/-0.316) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.615 (+/-0.238) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.667 (+/-0.316) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.617 (+/-0.238) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.641 (+/-0.325) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.592 (+/-0.320) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.617 (+/-0.327) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.617 (+/-0.326) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.640 (+/-0.323) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.664 (+/-0.314) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.682 (+/-0.296) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.666 (+/-0.316) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.615 (+/-0.238) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.667 (+/-0.316) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.617 (+/-0.238) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.641 (+/-0.325) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.592 (+/-0.320) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.633 (+/-0.318) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.617 (+/-0.326) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.640 (+/-0.323) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.664 (+/-0.314) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.682 (+/-0.296) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.666 (+/-0.316) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.615 (+/-0.238) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 10.0, 'kernel': 'linear'}
0.636 (+/-0.237) for {'C': 100.0, 'kernel': 'linear'}
0.637 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
0.636 (+/-0.240) for {'C': 10000.0, 'kernel': 'linear'}
0.610 (+/-0.245) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6822099925797676
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.747 (+/-0.502) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.747 (+/-0.502) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.747 (+/-0.502) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.797 (+/-0.492) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.797 (+/-0.492) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.797 (+/-0.492) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.797 (+/-0.492) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.747 (+/-0.502) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.747 (+/-0.502) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.747 (+/-0.502) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.747 (+/-0.502) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.798 (+/-0.492) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.798 (+/-0.492) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.798 (+/-0.492) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.798 (+/-0.492) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.798 (+/-0.492) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.848 (+/-0.460) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.798 (+/-0.492) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.798 (+/-0.492) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.798 (+/-0.492) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.797 (+/-0.492) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.798 (+/-0.492) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.768 (+/-0.475) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.748 (+/-0.449) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.723 (+/-0.461) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.664 (+/-0.449) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.689 (+/-0.436) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.704 (+/-0.427) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.669 (+/-0.365) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.652 (+/-0.374) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.659 (+/-0.368) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.608 (+/-0.309) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.618 (+/-0.299) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.798 (+/-0.492) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.723 (+/-0.417) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.723 (+/-0.461) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.666 (+/-0.441) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.664 (+/-0.389) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.697 (+/-0.420) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.658 (+/-0.373) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.644 (+/-0.381) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.625 (+/-0.305) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.587 (+/-0.313) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.610 (+/-0.298) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.798 (+/-0.492) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.723 (+/-0.417) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.721 (+/-0.463) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.666 (+/-0.441) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.660 (+/-0.389) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.690 (+/-0.431) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.653 (+/-0.374) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.643 (+/-0.382) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.629 (+/-0.304) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.586 (+/-0.313) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.611 (+/-0.299) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.764 (+/-0.477) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.723 (+/-0.417) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.721 (+/-0.463) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.657 (+/-0.451) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.660 (+/-0.389) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.665 (+/-0.383) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.653 (+/-0.374) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.643 (+/-0.382) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.629 (+/-0.304) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.586 (+/-0.313) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.611 (+/-0.299) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.798 (+/-0.492) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.723 (+/-0.417) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.721 (+/-0.463) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.657 (+/-0.451) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.660 (+/-0.389) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.672 (+/-0.372) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.656 (+/-0.372) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.643 (+/-0.382) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.629 (+/-0.304) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.586 (+/-0.313) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.611 (+/-0.299) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 1.0, 'kernel': 'linear'}
0.666 (+/-0.379) for {'C': 10.0, 'kernel': 'linear'}
0.629 (+/-0.313) for {'C': 100.0, 'kernel': 'linear'}
0.632 (+/-0.309) for {'C': 1000.0, 'kernel': 'linear'}
0.629 (+/-0.312) for {'C': 10000.0, 'kernel': 'linear'}
0.624 (+/-0.319) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.617 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.617 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.617 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.642 (+/-0.236) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.642 (+/-0.236) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.642 (+/-0.236) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.642 (+/-0.236) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.617 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.617 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.617 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.617 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.667 (+/-0.316) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.667 (+/-0.316) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.667 (+/-0.316) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.667 (+/-0.316) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.667 (+/-0.316) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.683 (+/-0.296) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.667 (+/-0.316) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.667 (+/-0.316) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.667 (+/-0.316) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.642 (+/-0.236) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.667 (+/-0.316) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.666 (+/-0.314) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.666 (+/-0.316) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.666 (+/-0.315) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.616 (+/-0.327) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.632 (+/-0.318) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.657 (+/-0.310) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.680 (+/-0.294) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.679 (+/-0.293) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.680 (+/-0.294) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.638 (+/-0.316) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.663 (+/-0.311) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.667 (+/-0.316) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.666 (+/-0.316) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.666 (+/-0.315) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.631 (+/-0.318) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.648 (+/-0.340) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.680 (+/-0.295) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.679 (+/-0.294) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.677 (+/-0.292) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.678 (+/-0.293) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.611 (+/-0.312) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.662 (+/-0.311) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.667 (+/-0.316) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.666 (+/-0.316) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.665 (+/-0.315) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.631 (+/-0.318) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.648 (+/-0.340) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.663 (+/-0.316) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.679 (+/-0.293) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.677 (+/-0.292) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.695 (+/-0.304) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.611 (+/-0.311) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.662 (+/-0.312) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.666 (+/-0.314) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.666 (+/-0.316) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.665 (+/-0.315) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.614 (+/-0.328) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.648 (+/-0.340) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.663 (+/-0.316) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.679 (+/-0.293) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.677 (+/-0.292) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.695 (+/-0.304) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.611 (+/-0.310) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.662 (+/-0.312) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.667 (+/-0.316) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.666 (+/-0.316) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 1.737800828749378e-07}
0.665 (+/-0.315) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 1.9054607179632464e-07}
0.614 (+/-0.328) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 2.0892961308540398e-07}
0.648 (+/-0.340) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 2.2908676527677748e-07}
0.680 (+/-0.294) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 2.511886431509577e-07}
0.679 (+/-0.293) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 2.754228703338165e-07}
0.677 (+/-0.292) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.695 (+/-0.304) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.611 (+/-0.310) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.662 (+/-0.312) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.662 (+/-0.314) for {'C': 10.0, 'kernel': 'linear'}
0.660 (+/-0.315) for {'C': 100.0, 'kernel': 'linear'}
0.637 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
0.636 (+/-0.240) for {'C': 10000.0, 'kernel': 'linear'}
0.610 (+/-0.245) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6947079314040729
第0轮loop中,tunemodel函数得到的最优参数是: ([{'C': array([1.00000000e+08, 1.58489319e+08, 2.51188643e+08, 3.98107171e+08,
6.30957344e+08, 1.00000000e+09, 1.58489319e+09, 2.51188643e+09,
3.98107171e+09, 6.30957344e+09, 1.00000000e+10]), 'kernel': ['rbf'], 'gamma': array([3.01995172e-07, 3.07609681e-07, 3.13328572e-07, 3.19153786e-07,
3.25087297e-07, 3.31131121e-07, 3.37287309e-07, 3.43557948e-07,
3.49945167e-07, 3.56451133e-07, 3.63078055e-07])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}], {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}, 0.6947079314040729)
这是第0次迭代微调C和gamma。
第0次迭代,得到delta: [9.99000000e+08 7.99424783e-08]
执行tunemodel()函数前,使用的grid search parameter是:
[{'C': array([1.00000000e+08, 1.58489319e+08, 2.51188643e+08, 3.98107171e+08,
6.30957344e+08, 1.00000000e+09, 1.58489319e+09, 2.51188643e+09,
3.98107171e+09, 6.30957344e+09, 1.00000000e+10]), 'kernel': ['rbf'], 'gamma': array([3.01995172e-07, 3.07609681e-07, 3.13328572e-07, 3.19153786e-07,
3.25087297e-07, 3.31131121e-07, 3.37287309e-07, 3.43557948e-07,
3.49945167e-07, 3.56451133e-07, 3.63078055e-07])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.671 (+/-0.433) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.690 (+/-0.414) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.076096814740707e-07}
0.688 (+/-0.425) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.1332857243155833e-07}
0.691 (+/-0.422) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.191537855100758e-07}
0.689 (+/-0.422) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.739 (+/-0.437) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.706 (+/-0.418) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.685 (+/-0.438) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.4355794789987457e-07}
0.681 (+/-0.373) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.499451670283571e-07}
0.723 (+/-0.417) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.564511334262439e-07}
0.714 (+/-0.418) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.671 (+/-0.433) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.689 (+/-0.415) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.076096814740707e-07}
0.688 (+/-0.425) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.1332857243155833e-07}
0.691 (+/-0.422) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.191537855100758e-07}
0.702 (+/-0.405) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.739 (+/-0.437) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.706 (+/-0.418) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.685 (+/-0.438) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.4355794789987457e-07}
0.681 (+/-0.373) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.499451670283571e-07}
0.714 (+/-0.418) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.564511334262439e-07}
0.714 (+/-0.418) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.671 (+/-0.433) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.690 (+/-0.414) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.076096814740707e-07}
0.688 (+/-0.425) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.1332857243155833e-07}
0.691 (+/-0.422) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.191537855100758e-07}
0.702 (+/-0.405) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.739 (+/-0.437) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.706 (+/-0.418) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.685 (+/-0.438) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.4355794789987457e-07}
0.681 (+/-0.373) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.499451670283571e-07}
0.714 (+/-0.418) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.564511334262439e-07}
0.714 (+/-0.418) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.671 (+/-0.433) for {'C': 398107170.5534974, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.689 (+/-0.415) for {'C': 398107170.5534974, 'kernel': 'rbf', 'gamma': 3.076096814740707e-07}
0.681 (+/-0.428) for {'C': 398107170.5534974, 'kernel': 'rbf', 'gamma': 3.1332857243155833e-07}
0.687 (+/-0.424) for {'C': 398107170.5534974, 'kernel': 'rbf', 'gamma': 3.191537855100758e-07}
0.702 (+/-0.405) for {'C': 398107170.5534974, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.739 (+/-0.437) for {'C': 398107170.5534974, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.706 (+/-0.418) for {'C': 398107170.5534974, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.685 (+/-0.438) for {'C': 398107170.5534974, 'kernel': 'rbf', 'gamma': 3.4355794789987457e-07}
0.681 (+/-0.373) for {'C': 398107170.5534974, 'kernel': 'rbf', 'gamma': 3.499451670283571e-07}
0.714 (+/-0.418) for {'C': 398107170.5534974, 'kernel': 'rbf', 'gamma': 3.564511334262439e-07}
0.714 (+/-0.418) for {'C': 398107170.5534974, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.671 (+/-0.433) for {'C': 630957344.4801937, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.689 (+/-0.415) for {'C': 630957344.4801937, 'kernel': 'rbf', 'gamma': 3.076096814740707e-07}
0.684 (+/-0.426) for {'C': 630957344.4801937, 'kernel': 'rbf', 'gamma': 3.1332857243155833e-07}
0.687 (+/-0.424) for {'C': 630957344.4801937, 'kernel': 'rbf', 'gamma': 3.191537855100758e-07}
0.702 (+/-0.405) for {'C': 630957344.4801937, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.739 (+/-0.437) for {'C': 630957344.4801937, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.706 (+/-0.418) for {'C': 630957344.4801937, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.660 (+/-0.390) for {'C': 630957344.4801937, 'kernel': 'rbf', 'gamma': 3.4355794789987457e-07}
0.681 (+/-0.373) for {'C': 630957344.4801937, 'kernel': 'rbf', 'gamma': 3.499451670283571e-07}
0.714 (+/-0.418) for {'C': 630957344.4801937, 'kernel': 'rbf', 'gamma': 3.564511334262439e-07}
0.714 (+/-0.418) for {'C': 630957344.4801937, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.671 (+/-0.433) for {'C': 1000000000.0000011, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.687 (+/-0.416) for {'C': 1000000000.0000011, 'kernel': 'rbf', 'gamma': 3.076096814740707e-07}
0.681 (+/-0.428) for {'C': 1000000000.0000011, 'kernel': 'rbf', 'gamma': 3.1332857243155833e-07}
0.687 (+/-0.424) for {'C': 1000000000.0000011, 'kernel': 'rbf', 'gamma': 3.191537855100758e-07}
0.702 (+/-0.405) for {'C': 1000000000.0000011, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.739 (+/-0.437) for {'C': 1000000000.0000011, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.706 (+/-0.418) for {'C': 1000000000.0000011, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.660 (+/-0.390) for {'C': 1000000000.0000011, 'kernel': 'rbf', 'gamma': 3.4355794789987457e-07}
0.677 (+/-0.374) for {'C': 1000000000.0000011, 'kernel': 'rbf', 'gamma': 3.499451670283571e-07}
0.714 (+/-0.418) for {'C': 1000000000.0000011, 'kernel': 'rbf', 'gamma': 3.564511334262439e-07}
0.714 (+/-0.418) for {'C': 1000000000.0000011, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.671 (+/-0.433) for {'C': 1584893192.461112, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.687 (+/-0.416) for {'C': 1584893192.461112, 'kernel': 'rbf', 'gamma': 3.076096814740707e-07}
0.684 (+/-0.426) for {'C': 1584893192.461112, 'kernel': 'rbf', 'gamma': 3.1332857243155833e-07}
0.687 (+/-0.424) for {'C': 1584893192.461112, 'kernel': 'rbf', 'gamma': 3.191537855100758e-07}
0.702 (+/-0.405) for {'C': 1584893192.461112, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.739 (+/-0.437) for {'C': 1584893192.461112, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.706 (+/-0.418) for {'C': 1584893192.461112, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.660 (+/-0.390) for {'C': 1584893192.461112, 'kernel': 'rbf', 'gamma': 3.4355794789987457e-07}
0.677 (+/-0.374) for {'C': 1584893192.461112, 'kernel': 'rbf', 'gamma': 3.499451670283571e-07}
0.714 (+/-0.418) for {'C': 1584893192.461112, 'kernel': 'rbf', 'gamma': 3.564511334262439e-07}
0.714 (+/-0.418) for {'C': 1584893192.461112, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.671 (+/-0.433) for {'C': 2511886431.5095787, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.687 (+/-0.416) for {'C': 2511886431.5095787, 'kernel': 'rbf', 'gamma': 3.076096814740707e-07}
0.684 (+/-0.426) for {'C': 2511886431.5095787, 'kernel': 'rbf', 'gamma': 3.1332857243155833e-07}
0.687 (+/-0.424) for {'C': 2511886431.5095787, 'kernel': 'rbf', 'gamma': 3.191537855100758e-07}
0.702 (+/-0.405) for {'C': 2511886431.5095787, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.739 (+/-0.437) for {'C': 2511886431.5095787, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.706 (+/-0.418) for {'C': 2511886431.5095787, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.660 (+/-0.390) for {'C': 2511886431.5095787, 'kernel': 'rbf', 'gamma': 3.4355794789987457e-07}
0.677 (+/-0.374) for {'C': 2511886431.5095787, 'kernel': 'rbf', 'gamma': 3.499451670283571e-07}
0.714 (+/-0.418) for {'C': 2511886431.5095787, 'kernel': 'rbf', 'gamma': 3.564511334262439e-07}
0.714 (+/-0.418) for {'C': 2511886431.5095787, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.671 (+/-0.433) for {'C': 3981071705.534972, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.689 (+/-0.415) for {'C': 3981071705.534972, 'kernel': 'rbf', 'gamma': 3.076096814740707e-07}
0.684 (+/-0.426) for {'C': 3981071705.534972, 'kernel': 'rbf', 'gamma': 3.1332857243155833e-07}
0.687 (+/-0.424) for {'C': 3981071705.534972, 'kernel': 'rbf', 'gamma': 3.191537855100758e-07}
0.702 (+/-0.405) for {'C': 3981071705.534972, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.739 (+/-0.437) for {'C': 3981071705.534972, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.706 (+/-0.418) for {'C': 3981071705.534972, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.660 (+/-0.390) for {'C': 3981071705.534972, 'kernel': 'rbf', 'gamma': 3.4355794789987457e-07}
0.677 (+/-0.374) for {'C': 3981071705.534972, 'kernel': 'rbf', 'gamma': 3.499451670283571e-07}
0.714 (+/-0.418) for {'C': 3981071705.534972, 'kernel': 'rbf', 'gamma': 3.564511334262439e-07}
0.714 (+/-0.418) for {'C': 3981071705.534972, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.671 (+/-0.433) for {'C': 6309573444.8019495, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.689 (+/-0.415) for {'C': 6309573444.8019495, 'kernel': 'rbf', 'gamma': 3.076096814740707e-07}
0.681 (+/-0.428) for {'C': 6309573444.8019495, 'kernel': 'rbf', 'gamma': 3.1332857243155833e-07}
0.687 (+/-0.424) for {'C': 6309573444.8019495, 'kernel': 'rbf', 'gamma': 3.191537855100758e-07}
0.702 (+/-0.405) for {'C': 6309573444.8019495, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.739 (+/-0.437) for {'C': 6309573444.8019495, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.706 (+/-0.418) for {'C': 6309573444.8019495, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.660 (+/-0.390) for {'C': 6309573444.8019495, 'kernel': 'rbf', 'gamma': 3.4355794789987457e-07}
0.677 (+/-0.374) for {'C': 6309573444.8019495, 'kernel': 'rbf', 'gamma': 3.499451670283571e-07}
0.714 (+/-0.418) for {'C': 6309573444.8019495, 'kernel': 'rbf', 'gamma': 3.564511334262439e-07}
0.714 (+/-0.418) for {'C': 6309573444.8019495, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.671 (+/-0.433) for {'C': 10000000000.000008, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.687 (+/-0.417) for {'C': 10000000000.000008, 'kernel': 'rbf', 'gamma': 3.076096814740707e-07}
0.681 (+/-0.428) for {'C': 10000000000.000008, 'kernel': 'rbf', 'gamma': 3.1332857243155833e-07}
0.687 (+/-0.424) for {'C': 10000000000.000008, 'kernel': 'rbf', 'gamma': 3.191537855100758e-07}
0.702 (+/-0.405) for {'C': 10000000000.000008, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.739 (+/-0.437) for {'C': 10000000000.000008, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.706 (+/-0.418) for {'C': 10000000000.000008, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.660 (+/-0.390) for {'C': 10000000000.000008, 'kernel': 'rbf', 'gamma': 3.4355794789987457e-07}
0.702 (+/-0.421) for {'C': 10000000000.000008, 'kernel': 'rbf', 'gamma': 3.499451670283571e-07}
0.714 (+/-0.418) for {'C': 10000000000.000008, 'kernel': 'rbf', 'gamma': 3.564511334262439e-07}
0.714 (+/-0.418) for {'C': 10000000000.000008, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'linear'}
0.706 (+/-0.383) for {'C': 10.0, 'kernel': 'linear'}
0.635 (+/-0.313) for {'C': 100.0, 'kernel': 'linear'}
0.632 (+/-0.309) for {'C': 1000.0, 'kernel': 'linear'}
0.629 (+/-0.312) for {'C': 10000.0, 'kernel': 'linear'}
0.624 (+/-0.319) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.664 (+/-0.314) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.681 (+/-0.295) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.076096814740707e-07}
0.665 (+/-0.316) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.1332857243155833e-07}
0.665 (+/-0.316) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.191537855100758e-07}
0.665 (+/-0.316) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.682 (+/-0.296) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.665 (+/-0.316) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.640 (+/-0.326) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.4355794789987457e-07}
0.665 (+/-0.316) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.499451670283571e-07}
0.666 (+/-0.316) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.564511334262439e-07}
0.666 (+/-0.316) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.664 (+/-0.314) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.681 (+/-0.294) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.076096814740707e-07}
0.665 (+/-0.316) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.1332857243155833e-07}
0.665 (+/-0.316) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.191537855100758e-07}
0.681 (+/-0.296) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.682 (+/-0.296) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.665 (+/-0.316) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.640 (+/-0.326) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.4355794789987457e-07}
0.665 (+/-0.316) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.499451670283571e-07}
0.666 (+/-0.316) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.564511334262439e-07}
0.666 (+/-0.316) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.664 (+/-0.314) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.681 (+/-0.295) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.076096814740707e-07}
0.665 (+/-0.316) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.1332857243155833e-07}
0.665 (+/-0.316) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.191537855100758e-07}
0.681 (+/-0.296) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.682 (+/-0.296) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.665 (+/-0.316) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.640 (+/-0.326) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.4355794789987457e-07}
0.665 (+/-0.316) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.499451670283571e-07}
0.666 (+/-0.316) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.564511334262439e-07}
0.666 (+/-0.316) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.664 (+/-0.314) for {'C': 398107170.5534974, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.681 (+/-0.294) for {'C': 398107170.5534974, 'kernel': 'rbf', 'gamma': 3.076096814740707e-07}
0.664 (+/-0.315) for {'C': 398107170.5534974, 'kernel': 'rbf', 'gamma': 3.1332857243155833e-07}
0.665 (+/-0.316) for {'C': 398107170.5534974, 'kernel': 'rbf', 'gamma': 3.191537855100758e-07}
0.681 (+/-0.296) for {'C': 398107170.5534974, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.682 (+/-0.296) for {'C': 398107170.5534974, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.665 (+/-0.316) for {'C': 398107170.5534974, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.640 (+/-0.326) for {'C': 398107170.5534974, 'kernel': 'rbf', 'gamma': 3.4355794789987457e-07}
0.665 (+/-0.316) for {'C': 398107170.5534974, 'kernel': 'rbf', 'gamma': 3.499451670283571e-07}
0.666 (+/-0.316) for {'C': 398107170.5534974, 'kernel': 'rbf', 'gamma': 3.564511334262439e-07}
0.666 (+/-0.316) for {'C': 398107170.5534974, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.664 (+/-0.314) for {'C': 630957344.4801937, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.681 (+/-0.294) for {'C': 630957344.4801937, 'kernel': 'rbf', 'gamma': 3.076096814740707e-07}
0.664 (+/-0.315) for {'C': 630957344.4801937, 'kernel': 'rbf', 'gamma': 3.1332857243155833e-07}
0.665 (+/-0.316) for {'C': 630957344.4801937, 'kernel': 'rbf', 'gamma': 3.191537855100758e-07}
0.681 (+/-0.296) for {'C': 630957344.4801937, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.682 (+/-0.296) for {'C': 630957344.4801937, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.665 (+/-0.316) for {'C': 630957344.4801937, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.640 (+/-0.326) for {'C': 630957344.4801937, 'kernel': 'rbf', 'gamma': 3.4355794789987457e-07}
0.665 (+/-0.316) for {'C': 630957344.4801937, 'kernel': 'rbf', 'gamma': 3.499451670283571e-07}
0.666 (+/-0.316) for {'C': 630957344.4801937, 'kernel': 'rbf', 'gamma': 3.564511334262439e-07}
0.666 (+/-0.316) for {'C': 630957344.4801937, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.664 (+/-0.314) for {'C': 1000000000.0000011, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.681 (+/-0.294) for {'C': 1000000000.0000011, 'kernel': 'rbf', 'gamma': 3.076096814740707e-07}
0.664 (+/-0.315) for {'C': 1000000000.0000011, 'kernel': 'rbf', 'gamma': 3.1332857243155833e-07}
0.665 (+/-0.316) for {'C': 1000000000.0000011, 'kernel': 'rbf', 'gamma': 3.191537855100758e-07}
0.681 (+/-0.296) for {'C': 1000000000.0000011, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.682 (+/-0.296) for {'C': 1000000000.0000011, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.665 (+/-0.316) for {'C': 1000000000.0000011, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.640 (+/-0.326) for {'C': 1000000000.0000011, 'kernel': 'rbf', 'gamma': 3.4355794789987457e-07}
0.665 (+/-0.316) for {'C': 1000000000.0000011, 'kernel': 'rbf', 'gamma': 3.499451670283571e-07}
0.666 (+/-0.316) for {'C': 1000000000.0000011, 'kernel': 'rbf', 'gamma': 3.564511334262439e-07}
0.666 (+/-0.316) for {'C': 1000000000.0000011, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.664 (+/-0.314) for {'C': 1584893192.461112, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.681 (+/-0.294) for {'C': 1584893192.461112, 'kernel': 'rbf', 'gamma': 3.076096814740707e-07}
0.664 (+/-0.315) for {'C': 1584893192.461112, 'kernel': 'rbf', 'gamma': 3.1332857243155833e-07}
0.665 (+/-0.316) for {'C': 1584893192.461112, 'kernel': 'rbf', 'gamma': 3.191537855100758e-07}
0.681 (+/-0.296) for {'C': 1584893192.461112, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.682 (+/-0.296) for {'C': 1584893192.461112, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.665 (+/-0.316) for {'C': 1584893192.461112, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.640 (+/-0.326) for {'C': 1584893192.461112, 'kernel': 'rbf', 'gamma': 3.4355794789987457e-07}
0.665 (+/-0.316) for {'C': 1584893192.461112, 'kernel': 'rbf', 'gamma': 3.499451670283571e-07}
0.666 (+/-0.316) for {'C': 1584893192.461112, 'kernel': 'rbf', 'gamma': 3.564511334262439e-07}
0.666 (+/-0.316) for {'C': 1584893192.461112, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.664 (+/-0.314) for {'C': 2511886431.5095787, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.681 (+/-0.294) for {'C': 2511886431.5095787, 'kernel': 'rbf', 'gamma': 3.076096814740707e-07}
0.664 (+/-0.315) for {'C': 2511886431.5095787, 'kernel': 'rbf', 'gamma': 3.1332857243155833e-07}
0.665 (+/-0.316) for {'C': 2511886431.5095787, 'kernel': 'rbf', 'gamma': 3.191537855100758e-07}
0.681 (+/-0.296) for {'C': 2511886431.5095787, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.682 (+/-0.296) for {'C': 2511886431.5095787, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.665 (+/-0.316) for {'C': 2511886431.5095787, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.640 (+/-0.326) for {'C': 2511886431.5095787, 'kernel': 'rbf', 'gamma': 3.4355794789987457e-07}
0.665 (+/-0.316) for {'C': 2511886431.5095787, 'kernel': 'rbf', 'gamma': 3.499451670283571e-07}
0.666 (+/-0.316) for {'C': 2511886431.5095787, 'kernel': 'rbf', 'gamma': 3.564511334262439e-07}
0.666 (+/-0.316) for {'C': 2511886431.5095787, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.664 (+/-0.314) for {'C': 3981071705.534972, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.681 (+/-0.294) for {'C': 3981071705.534972, 'kernel': 'rbf', 'gamma': 3.076096814740707e-07}
0.664 (+/-0.315) for {'C': 3981071705.534972, 'kernel': 'rbf', 'gamma': 3.1332857243155833e-07}
0.665 (+/-0.316) for {'C': 3981071705.534972, 'kernel': 'rbf', 'gamma': 3.191537855100758e-07}
0.681 (+/-0.296) for {'C': 3981071705.534972, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.682 (+/-0.296) for {'C': 3981071705.534972, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.665 (+/-0.316) for {'C': 3981071705.534972, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.640 (+/-0.326) for {'C': 3981071705.534972, 'kernel': 'rbf', 'gamma': 3.4355794789987457e-07}
0.665 (+/-0.316) for {'C': 3981071705.534972, 'kernel': 'rbf', 'gamma': 3.499451670283571e-07}
0.666 (+/-0.316) for {'C': 3981071705.534972, 'kernel': 'rbf', 'gamma': 3.564511334262439e-07}
0.666 (+/-0.316) for {'C': 3981071705.534972, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.664 (+/-0.314) for {'C': 6309573444.8019495, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.681 (+/-0.294) for {'C': 6309573444.8019495, 'kernel': 'rbf', 'gamma': 3.076096814740707e-07}
0.664 (+/-0.315) for {'C': 6309573444.8019495, 'kernel': 'rbf', 'gamma': 3.1332857243155833e-07}
0.665 (+/-0.316) for {'C': 6309573444.8019495, 'kernel': 'rbf', 'gamma': 3.191537855100758e-07}
0.681 (+/-0.296) for {'C': 6309573444.8019495, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.682 (+/-0.296) for {'C': 6309573444.8019495, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.665 (+/-0.316) for {'C': 6309573444.8019495, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.640 (+/-0.326) for {'C': 6309573444.8019495, 'kernel': 'rbf', 'gamma': 3.4355794789987457e-07}
0.665 (+/-0.316) for {'C': 6309573444.8019495, 'kernel': 'rbf', 'gamma': 3.499451670283571e-07}
0.666 (+/-0.316) for {'C': 6309573444.8019495, 'kernel': 'rbf', 'gamma': 3.564511334262439e-07}
0.666 (+/-0.316) for {'C': 6309573444.8019495, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.664 (+/-0.314) for {'C': 10000000000.000008, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.680 (+/-0.294) for {'C': 10000000000.000008, 'kernel': 'rbf', 'gamma': 3.076096814740707e-07}
0.664 (+/-0.315) for {'C': 10000000000.000008, 'kernel': 'rbf', 'gamma': 3.1332857243155833e-07}
0.665 (+/-0.316) for {'C': 10000000000.000008, 'kernel': 'rbf', 'gamma': 3.191537855100758e-07}
0.681 (+/-0.296) for {'C': 10000000000.000008, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.682 (+/-0.296) for {'C': 10000000000.000008, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.665 (+/-0.316) for {'C': 10000000000.000008, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.640 (+/-0.326) for {'C': 10000000000.000008, 'kernel': 'rbf', 'gamma': 3.4355794789987457e-07}
0.665 (+/-0.316) for {'C': 10000000000.000008, 'kernel': 'rbf', 'gamma': 3.499451670283571e-07}
0.666 (+/-0.316) for {'C': 10000000000.000008, 'kernel': 'rbf', 'gamma': 3.564511334262439e-07}
0.666 (+/-0.316) for {'C': 10000000000.000008, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 10.0, 'kernel': 'linear'}
0.636 (+/-0.237) for {'C': 100.0, 'kernel': 'linear'}
0.637 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
0.636 (+/-0.240) for {'C': 10000.0, 'kernel': 'linear'}
0.610 (+/-0.245) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6820497361695111
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.644 (+/-0.381) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.615 (+/-0.298) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.076096814740707e-07}
0.644 (+/-0.382) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.1332857243155833e-07}
0.630 (+/-0.325) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.191537855100758e-07}
0.620 (+/-0.309) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.625 (+/-0.305) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.617 (+/-0.292) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.610 (+/-0.296) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.4355794789987457e-07}
0.608 (+/-0.300) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.499451670283571e-07}
0.609 (+/-0.300) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.564511334262439e-07}
0.587 (+/-0.313) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.643 (+/-0.382) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.614 (+/-0.299) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.076096814740707e-07}
0.657 (+/-0.370) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.1332857243155833e-07}
0.622 (+/-0.308) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.191537855100758e-07}
0.620 (+/-0.310) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.630 (+/-0.303) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.617 (+/-0.292) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.610 (+/-0.296) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.4355794789987457e-07}
0.607 (+/-0.300) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.499451670283571e-07}
0.609 (+/-0.300) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.564511334262439e-07}
0.587 (+/-0.313) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.643 (+/-0.382) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.614 (+/-0.299) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.076096814740707e-07}
0.657 (+/-0.370) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.1332857243155833e-07}
0.621 (+/-0.309) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.191537855100758e-07}
0.619 (+/-0.310) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.630 (+/-0.303) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.617 (+/-0.292) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.610 (+/-0.296) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.4355794789987457e-07}
0.605 (+/-0.301) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.499451670283571e-07}
0.609 (+/-0.300) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.564511334262439e-07}
0.586 (+/-0.313) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.643 (+/-0.382) for {'C': 398107170.5534974, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.623 (+/-0.308) for {'C': 398107170.5534974, 'kernel': 'rbf', 'gamma': 3.076096814740707e-07}
0.657 (+/-0.370) for {'C': 398107170.5534974, 'kernel': 'rbf', 'gamma': 3.1332857243155833e-07}
0.621 (+/-0.309) for {'C': 398107170.5534974, 'kernel': 'rbf', 'gamma': 3.191537855100758e-07}
0.619 (+/-0.310) for {'C': 398107170.5534974, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.629 (+/-0.304) for {'C': 398107170.5534974, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.617 (+/-0.292) for {'C': 398107170.5534974, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.610 (+/-0.296) for {'C': 398107170.5534974, 'kernel': 'rbf', 'gamma': 3.4355794789987457e-07}
0.606 (+/-0.301) for {'C': 398107170.5534974, 'kernel': 'rbf', 'gamma': 3.499451670283571e-07}
0.609 (+/-0.300) for {'C': 398107170.5534974, 'kernel': 'rbf', 'gamma': 3.564511334262439e-07}
0.586 (+/-0.313) for {'C': 398107170.5534974, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.643 (+/-0.382) for {'C': 630957344.4801937, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.614 (+/-0.299) for {'C': 630957344.4801937, 'kernel': 'rbf', 'gamma': 3.076096814740707e-07}
0.656 (+/-0.371) for {'C': 630957344.4801937, 'kernel': 'rbf', 'gamma': 3.1332857243155833e-07}
0.621 (+/-0.309) for {'C': 630957344.4801937, 'kernel': 'rbf', 'gamma': 3.191537855100758e-07}
0.619 (+/-0.310) for {'C': 630957344.4801937, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.625 (+/-0.306) for {'C': 630957344.4801937, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.617 (+/-0.292) for {'C': 630957344.4801937, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.609 (+/-0.297) for {'C': 630957344.4801937, 'kernel': 'rbf', 'gamma': 3.4355794789987457e-07}
0.606 (+/-0.301) for {'C': 630957344.4801937, 'kernel': 'rbf', 'gamma': 3.499451670283571e-07}
0.609 (+/-0.300) for {'C': 630957344.4801937, 'kernel': 'rbf', 'gamma': 3.564511334262439e-07}
0.586 (+/-0.313) for {'C': 630957344.4801937, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.643 (+/-0.382) for {'C': 1000000000.0000011, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.614 (+/-0.299) for {'C': 1000000000.0000011, 'kernel': 'rbf', 'gamma': 3.076096814740707e-07}
0.656 (+/-0.371) for {'C': 1000000000.0000011, 'kernel': 'rbf', 'gamma': 3.1332857243155833e-07}
0.621 (+/-0.309) for {'C': 1000000000.0000011, 'kernel': 'rbf', 'gamma': 3.191537855100758e-07}
0.619 (+/-0.310) for {'C': 1000000000.0000011, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.629 (+/-0.304) for {'C': 1000000000.0000011, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.617 (+/-0.292) for {'C': 1000000000.0000011, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.609 (+/-0.297) for {'C': 1000000000.0000011, 'kernel': 'rbf', 'gamma': 3.4355794789987457e-07}
0.605 (+/-0.301) for {'C': 1000000000.0000011, 'kernel': 'rbf', 'gamma': 3.499451670283571e-07}
0.609 (+/-0.300) for {'C': 1000000000.0000011, 'kernel': 'rbf', 'gamma': 3.564511334262439e-07}
0.586 (+/-0.313) for {'C': 1000000000.0000011, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.643 (+/-0.382) for {'C': 1584893192.461112, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.614 (+/-0.299) for {'C': 1584893192.461112, 'kernel': 'rbf', 'gamma': 3.076096814740707e-07}
0.655 (+/-0.372) for {'C': 1584893192.461112, 'kernel': 'rbf', 'gamma': 3.1332857243155833e-07}
0.621 (+/-0.309) for {'C': 1584893192.461112, 'kernel': 'rbf', 'gamma': 3.191537855100758e-07}
0.619 (+/-0.310) for {'C': 1584893192.461112, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.625 (+/-0.306) for {'C': 1584893192.461112, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.614 (+/-0.292) for {'C': 1584893192.461112, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.609 (+/-0.297) for {'C': 1584893192.461112, 'kernel': 'rbf', 'gamma': 3.4355794789987457e-07}
0.606 (+/-0.301) for {'C': 1584893192.461112, 'kernel': 'rbf', 'gamma': 3.499451670283571e-07}
0.608 (+/-0.301) for {'C': 1584893192.461112, 'kernel': 'rbf', 'gamma': 3.564511334262439e-07}
0.586 (+/-0.313) for {'C': 1584893192.461112, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.643 (+/-0.382) for {'C': 2511886431.5095787, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.614 (+/-0.299) for {'C': 2511886431.5095787, 'kernel': 'rbf', 'gamma': 3.076096814740707e-07}
0.656 (+/-0.371) for {'C': 2511886431.5095787, 'kernel': 'rbf', 'gamma': 3.1332857243155833e-07}
0.621 (+/-0.309) for {'C': 2511886431.5095787, 'kernel': 'rbf', 'gamma': 3.191537855100758e-07}
0.619 (+/-0.310) for {'C': 2511886431.5095787, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.625 (+/-0.306) for {'C': 2511886431.5095787, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.614 (+/-0.292) for {'C': 2511886431.5095787, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.609 (+/-0.297) for {'C': 2511886431.5095787, 'kernel': 'rbf', 'gamma': 3.4355794789987457e-07}
0.606 (+/-0.301) for {'C': 2511886431.5095787, 'kernel': 'rbf', 'gamma': 3.499451670283571e-07}
0.608 (+/-0.301) for {'C': 2511886431.5095787, 'kernel': 'rbf', 'gamma': 3.564511334262439e-07}
0.586 (+/-0.313) for {'C': 2511886431.5095787, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.643 (+/-0.382) for {'C': 3981071705.534972, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.614 (+/-0.299) for {'C': 3981071705.534972, 'kernel': 'rbf', 'gamma': 3.076096814740707e-07}
0.655 (+/-0.372) for {'C': 3981071705.534972, 'kernel': 'rbf', 'gamma': 3.1332857243155833e-07}
0.621 (+/-0.309) for {'C': 3981071705.534972, 'kernel': 'rbf', 'gamma': 3.191537855100758e-07}
0.619 (+/-0.310) for {'C': 3981071705.534972, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.625 (+/-0.306) for {'C': 3981071705.534972, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.614 (+/-0.292) for {'C': 3981071705.534972, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.609 (+/-0.297) for {'C': 3981071705.534972, 'kernel': 'rbf', 'gamma': 3.4355794789987457e-07}
0.605 (+/-0.301) for {'C': 3981071705.534972, 'kernel': 'rbf', 'gamma': 3.499451670283571e-07}
0.608 (+/-0.301) for {'C': 3981071705.534972, 'kernel': 'rbf', 'gamma': 3.564511334262439e-07}
0.586 (+/-0.313) for {'C': 3981071705.534972, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.643 (+/-0.382) for {'C': 6309573444.8019495, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.614 (+/-0.299) for {'C': 6309573444.8019495, 'kernel': 'rbf', 'gamma': 3.076096814740707e-07}
0.655 (+/-0.372) for {'C': 6309573444.8019495, 'kernel': 'rbf', 'gamma': 3.1332857243155833e-07}
0.621 (+/-0.309) for {'C': 6309573444.8019495, 'kernel': 'rbf', 'gamma': 3.191537855100758e-07}
0.619 (+/-0.310) for {'C': 6309573444.8019495, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.629 (+/-0.304) for {'C': 6309573444.8019495, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.617 (+/-0.292) for {'C': 6309573444.8019495, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.609 (+/-0.297) for {'C': 6309573444.8019495, 'kernel': 'rbf', 'gamma': 3.4355794789987457e-07}
0.605 (+/-0.302) for {'C': 6309573444.8019495, 'kernel': 'rbf', 'gamma': 3.499451670283571e-07}
0.608 (+/-0.301) for {'C': 6309573444.8019495, 'kernel': 'rbf', 'gamma': 3.564511334262439e-07}
0.586 (+/-0.313) for {'C': 6309573444.8019495, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.643 (+/-0.382) for {'C': 10000000000.000008, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.614 (+/-0.299) for {'C': 10000000000.000008, 'kernel': 'rbf', 'gamma': 3.076096814740707e-07}
0.655 (+/-0.372) for {'C': 10000000000.000008, 'kernel': 'rbf', 'gamma': 3.1332857243155833e-07}
0.621 (+/-0.309) for {'C': 10000000000.000008, 'kernel': 'rbf', 'gamma': 3.191537855100758e-07}
0.619 (+/-0.310) for {'C': 10000000000.000008, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.625 (+/-0.306) for {'C': 10000000000.000008, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.617 (+/-0.292) for {'C': 10000000000.000008, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.609 (+/-0.297) for {'C': 10000000000.000008, 'kernel': 'rbf', 'gamma': 3.4355794789987457e-07}
0.605 (+/-0.302) for {'C': 10000000000.000008, 'kernel': 'rbf', 'gamma': 3.499451670283571e-07}
0.608 (+/-0.301) for {'C': 10000000000.000008, 'kernel': 'rbf', 'gamma': 3.564511334262439e-07}
0.586 (+/-0.313) for {'C': 10000000000.000008, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 1.0, 'kernel': 'linear'}
0.666 (+/-0.379) for {'C': 10.0, 'kernel': 'linear'}
0.629 (+/-0.313) for {'C': 100.0, 'kernel': 'linear'}
0.632 (+/-0.309) for {'C': 1000.0, 'kernel': 'linear'}
0.629 (+/-0.312) for {'C': 10000.0, 'kernel': 'linear'}
0.624 (+/-0.319) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.20 0.17 0.18 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.677 (+/-0.292) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.677 (+/-0.291) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.076096814740707e-07}
0.663 (+/-0.314) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.1332857243155833e-07}
0.678 (+/-0.293) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.191537855100758e-07}
0.677 (+/-0.292) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.678 (+/-0.293) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.678 (+/-0.285) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.675 (+/-0.276) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.4355794789987457e-07}
0.661 (+/-0.309) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.499451670283571e-07}
0.661 (+/-0.302) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.564511334262439e-07}
0.611 (+/-0.312) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.677 (+/-0.292) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.677 (+/-0.291) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.076096814740707e-07}
0.680 (+/-0.294) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.1332857243155833e-07}
0.678 (+/-0.292) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.191537855100758e-07}
0.677 (+/-0.292) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.695 (+/-0.304) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.678 (+/-0.285) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.675 (+/-0.275) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.4355794789987457e-07}
0.661 (+/-0.308) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.499451670283571e-07}
0.661 (+/-0.302) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.564511334262439e-07}
0.611 (+/-0.312) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.677 (+/-0.292) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.677 (+/-0.291) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.076096814740707e-07}
0.680 (+/-0.294) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.1332857243155833e-07}
0.678 (+/-0.292) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.191537855100758e-07}
0.677 (+/-0.291) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.695 (+/-0.304) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.678 (+/-0.285) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.675 (+/-0.275) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.4355794789987457e-07}
0.660 (+/-0.308) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.499451670283571e-07}
0.661 (+/-0.302) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.564511334262439e-07}
0.611 (+/-0.311) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.677 (+/-0.292) for {'C': 398107170.5534974, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.677 (+/-0.291) for {'C': 398107170.5534974, 'kernel': 'rbf', 'gamma': 3.076096814740707e-07}
0.680 (+/-0.294) for {'C': 398107170.5534974, 'kernel': 'rbf', 'gamma': 3.1332857243155833e-07}
0.678 (+/-0.292) for {'C': 398107170.5534974, 'kernel': 'rbf', 'gamma': 3.191537855100758e-07}
0.677 (+/-0.291) for {'C': 398107170.5534974, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.695 (+/-0.304) for {'C': 398107170.5534974, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.678 (+/-0.285) for {'C': 398107170.5534974, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.675 (+/-0.275) for {'C': 398107170.5534974, 'kernel': 'rbf', 'gamma': 3.4355794789987457e-07}
0.660 (+/-0.308) for {'C': 398107170.5534974, 'kernel': 'rbf', 'gamma': 3.499451670283571e-07}
0.661 (+/-0.302) for {'C': 398107170.5534974, 'kernel': 'rbf', 'gamma': 3.564511334262439e-07}
0.611 (+/-0.311) for {'C': 398107170.5534974, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.677 (+/-0.292) for {'C': 630957344.4801937, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.677 (+/-0.291) for {'C': 630957344.4801937, 'kernel': 'rbf', 'gamma': 3.076096814740707e-07}
0.679 (+/-0.294) for {'C': 630957344.4801937, 'kernel': 'rbf', 'gamma': 3.1332857243155833e-07}
0.678 (+/-0.292) for {'C': 630957344.4801937, 'kernel': 'rbf', 'gamma': 3.191537855100758e-07}
0.677 (+/-0.291) for {'C': 630957344.4801937, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.678 (+/-0.292) for {'C': 630957344.4801937, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.678 (+/-0.285) for {'C': 630957344.4801937, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.675 (+/-0.275) for {'C': 630957344.4801937, 'kernel': 'rbf', 'gamma': 3.4355794789987457e-07}
0.660 (+/-0.308) for {'C': 630957344.4801937, 'kernel': 'rbf', 'gamma': 3.499451670283571e-07}
0.661 (+/-0.302) for {'C': 630957344.4801937, 'kernel': 'rbf', 'gamma': 3.564511334262439e-07}
0.611 (+/-0.311) for {'C': 630957344.4801937, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.677 (+/-0.292) for {'C': 1000000000.0000011, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.677 (+/-0.291) for {'C': 1000000000.0000011, 'kernel': 'rbf', 'gamma': 3.076096814740707e-07}
0.679 (+/-0.294) for {'C': 1000000000.0000011, 'kernel': 'rbf', 'gamma': 3.1332857243155833e-07}
0.678 (+/-0.292) for {'C': 1000000000.0000011, 'kernel': 'rbf', 'gamma': 3.191537855100758e-07}
0.677 (+/-0.291) for {'C': 1000000000.0000011, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.695 (+/-0.304) for {'C': 1000000000.0000011, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.678 (+/-0.285) for {'C': 1000000000.0000011, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.675 (+/-0.275) for {'C': 1000000000.0000011, 'kernel': 'rbf', 'gamma': 3.4355794789987457e-07}
0.660 (+/-0.308) for {'C': 1000000000.0000011, 'kernel': 'rbf', 'gamma': 3.499451670283571e-07}
0.661 (+/-0.302) for {'C': 1000000000.0000011, 'kernel': 'rbf', 'gamma': 3.564511334262439e-07}
0.611 (+/-0.311) for {'C': 1000000000.0000011, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.677 (+/-0.292) for {'C': 1584893192.461112, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.677 (+/-0.291) for {'C': 1584893192.461112, 'kernel': 'rbf', 'gamma': 3.076096814740707e-07}
0.679 (+/-0.294) for {'C': 1584893192.461112, 'kernel': 'rbf', 'gamma': 3.1332857243155833e-07}
0.678 (+/-0.292) for {'C': 1584893192.461112, 'kernel': 'rbf', 'gamma': 3.191537855100758e-07}
0.677 (+/-0.291) for {'C': 1584893192.461112, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.678 (+/-0.292) for {'C': 1584893192.461112, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.677 (+/-0.285) for {'C': 1584893192.461112, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.675 (+/-0.275) for {'C': 1584893192.461112, 'kernel': 'rbf', 'gamma': 3.4355794789987457e-07}
0.660 (+/-0.308) for {'C': 1584893192.461112, 'kernel': 'rbf', 'gamma': 3.499451670283571e-07}
0.660 (+/-0.302) for {'C': 1584893192.461112, 'kernel': 'rbf', 'gamma': 3.564511334262439e-07}
0.611 (+/-0.310) for {'C': 1584893192.461112, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.677 (+/-0.292) for {'C': 2511886431.5095787, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.677 (+/-0.291) for {'C': 2511886431.5095787, 'kernel': 'rbf', 'gamma': 3.076096814740707e-07}
0.679 (+/-0.294) for {'C': 2511886431.5095787, 'kernel': 'rbf', 'gamma': 3.1332857243155833e-07}
0.678 (+/-0.292) for {'C': 2511886431.5095787, 'kernel': 'rbf', 'gamma': 3.191537855100758e-07}
0.677 (+/-0.291) for {'C': 2511886431.5095787, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.678 (+/-0.292) for {'C': 2511886431.5095787, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.677 (+/-0.285) for {'C': 2511886431.5095787, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.675 (+/-0.275) for {'C': 2511886431.5095787, 'kernel': 'rbf', 'gamma': 3.4355794789987457e-07}
0.660 (+/-0.308) for {'C': 2511886431.5095787, 'kernel': 'rbf', 'gamma': 3.499451670283571e-07}
0.660 (+/-0.302) for {'C': 2511886431.5095787, 'kernel': 'rbf', 'gamma': 3.564511334262439e-07}
0.611 (+/-0.310) for {'C': 2511886431.5095787, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.677 (+/-0.292) for {'C': 3981071705.534972, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.677 (+/-0.291) for {'C': 3981071705.534972, 'kernel': 'rbf', 'gamma': 3.076096814740707e-07}
0.679 (+/-0.294) for {'C': 3981071705.534972, 'kernel': 'rbf', 'gamma': 3.1332857243155833e-07}
0.678 (+/-0.292) for {'C': 3981071705.534972, 'kernel': 'rbf', 'gamma': 3.191537855100758e-07}
0.677 (+/-0.291) for {'C': 3981071705.534972, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.678 (+/-0.292) for {'C': 3981071705.534972, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.677 (+/-0.285) for {'C': 3981071705.534972, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.675 (+/-0.275) for {'C': 3981071705.534972, 'kernel': 'rbf', 'gamma': 3.4355794789987457e-07}
0.660 (+/-0.308) for {'C': 3981071705.534972, 'kernel': 'rbf', 'gamma': 3.499451670283571e-07}
0.660 (+/-0.302) for {'C': 3981071705.534972, 'kernel': 'rbf', 'gamma': 3.564511334262439e-07}
0.611 (+/-0.310) for {'C': 3981071705.534972, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.677 (+/-0.292) for {'C': 6309573444.8019495, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.677 (+/-0.291) for {'C': 6309573444.8019495, 'kernel': 'rbf', 'gamma': 3.076096814740707e-07}
0.679 (+/-0.294) for {'C': 6309573444.8019495, 'kernel': 'rbf', 'gamma': 3.1332857243155833e-07}
0.678 (+/-0.292) for {'C': 6309573444.8019495, 'kernel': 'rbf', 'gamma': 3.191537855100758e-07}
0.677 (+/-0.291) for {'C': 6309573444.8019495, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.695 (+/-0.304) for {'C': 6309573444.8019495, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.678 (+/-0.285) for {'C': 6309573444.8019495, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.675 (+/-0.275) for {'C': 6309573444.8019495, 'kernel': 'rbf', 'gamma': 3.4355794789987457e-07}
0.660 (+/-0.307) for {'C': 6309573444.8019495, 'kernel': 'rbf', 'gamma': 3.499451670283571e-07}
0.660 (+/-0.302) for {'C': 6309573444.8019495, 'kernel': 'rbf', 'gamma': 3.564511334262439e-07}
0.611 (+/-0.311) for {'C': 6309573444.8019495, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.677 (+/-0.292) for {'C': 10000000000.000008, 'kernel': 'rbf', 'gamma': 3.019951720402016e-07}
0.677 (+/-0.291) for {'C': 10000000000.000008, 'kernel': 'rbf', 'gamma': 3.076096814740707e-07}
0.679 (+/-0.294) for {'C': 10000000000.000008, 'kernel': 'rbf', 'gamma': 3.1332857243155833e-07}
0.678 (+/-0.292) for {'C': 10000000000.000008, 'kernel': 'rbf', 'gamma': 3.191537855100758e-07}
0.677 (+/-0.291) for {'C': 10000000000.000008, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.678 (+/-0.292) for {'C': 10000000000.000008, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.678 (+/-0.285) for {'C': 10000000000.000008, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.675 (+/-0.275) for {'C': 10000000000.000008, 'kernel': 'rbf', 'gamma': 3.4355794789987457e-07}
0.660 (+/-0.307) for {'C': 10000000000.000008, 'kernel': 'rbf', 'gamma': 3.499451670283571e-07}
0.660 (+/-0.302) for {'C': 10000000000.000008, 'kernel': 'rbf', 'gamma': 3.564511334262439e-07}
0.611 (+/-0.310) for {'C': 10000000000.000008, 'kernel': 'rbf', 'gamma': 3.630780547701009e-07}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.662 (+/-0.314) for {'C': 10.0, 'kernel': 'linear'}
0.660 (+/-0.315) for {'C': 100.0, 'kernel': 'linear'}
0.637 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
0.636 (+/-0.240) for {'C': 10000.0, 'kernel': 'linear'}
0.610 (+/-0.245) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6948687031082529
第1轮loop中,tunemodel函数得到的最优参数是: ([{'C': array([1.00000000e+08, 1.09647820e+08, 1.20226443e+08, 1.31825674e+08,
1.44543977e+08, 1.58489319e+08, 1.73780083e+08, 1.90546072e+08,
2.08929613e+08, 2.29086765e+08, 2.51188643e+08]), 'kernel': ['rbf'], 'gamma': array([3.25087297e-07, 3.26287172e-07, 3.27491476e-07, 3.28700224e-07,
3.29913434e-07, 3.31131121e-07, 3.32353304e-07, 3.33579997e-07,
3.34811217e-07, 3.36046982e-07, 3.37287309e-07])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}], {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}, 0.6948687031082529)
这是第1次迭代微调C和gamma。
第1次迭代,得到delta: [8.41510681e+08 0.00000000e+00]
执行tunemodel()函数前,使用的grid search parameter是:
[{'C': array([1.00000000e+08, 1.09647820e+08, 1.20226443e+08, 1.31825674e+08,
1.44543977e+08, 1.58489319e+08, 1.73780083e+08, 1.90546072e+08,
2.08929613e+08, 2.29086765e+08, 2.51188643e+08]), 'kernel': ['rbf'], 'gamma': array([3.25087297e-07, 3.26287172e-07, 3.27491476e-07, 3.28700224e-07,
3.29913434e-07, 3.31131121e-07, 3.32353304e-07, 3.33579997e-07,
3.34811217e-07, 3.36046982e-07, 3.37287309e-07])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.689 (+/-0.422) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.687 (+/-0.424) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.2628717214308483e-07}
0.689 (+/-0.422) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.2749147555557826e-07}
0.706 (+/-0.403) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.287002239687748e-07}
0.739 (+/-0.437) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.299134337888643e-07}
0.739 (+/-0.437) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.739 (+/-0.437) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.3235330357747726e-07}
0.689 (+/-0.421) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.3357999666204803e-07}
0.744 (+/-0.433) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.3481121738605415e-07}
0.694 (+/-0.355) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.3604698246069916e-07}
0.706 (+/-0.418) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.689 (+/-0.422) for {'C': 109647819.61431846, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.687 (+/-0.424) for {'C': 109647819.61431846, 'kernel': 'rbf', 'gamma': 3.2628717214308483e-07}
0.689 (+/-0.422) for {'C': 109647819.61431846, 'kernel': 'rbf', 'gamma': 3.2749147555557826e-07}
0.706 (+/-0.403) for {'C': 109647819.61431846, 'kernel': 'rbf', 'gamma': 3.287002239687748e-07}
0.739 (+/-0.437) for {'C': 109647819.61431846, 'kernel': 'rbf', 'gamma': 3.299134337888643e-07}
0.739 (+/-0.437) for {'C': 109647819.61431846, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.739 (+/-0.437) for {'C': 109647819.61431846, 'kernel': 'rbf', 'gamma': 3.3235330357747726e-07}
0.689 (+/-0.421) for {'C': 109647819.61431846, 'kernel': 'rbf', 'gamma': 3.3357999666204803e-07}
0.743 (+/-0.433) for {'C': 109647819.61431846, 'kernel': 'rbf', 'gamma': 3.3481121738605415e-07}
0.694 (+/-0.355) for {'C': 109647819.61431846, 'kernel': 'rbf', 'gamma': 3.3604698246069916e-07}
0.706 (+/-0.418) for {'C': 109647819.61431846, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.702 (+/-0.405) for {'C': 120226443.46174131, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.687 (+/-0.424) for {'C': 120226443.46174131, 'kernel': 'rbf', 'gamma': 3.2628717214308483e-07}
0.689 (+/-0.422) for {'C': 120226443.46174131, 'kernel': 'rbf', 'gamma': 3.2749147555557826e-07}
0.706 (+/-0.403) for {'C': 120226443.46174131, 'kernel': 'rbf', 'gamma': 3.287002239687748e-07}
0.739 (+/-0.437) for {'C': 120226443.46174131, 'kernel': 'rbf', 'gamma': 3.299134337888643e-07}
0.739 (+/-0.437) for {'C': 120226443.46174131, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.739 (+/-0.437) for {'C': 120226443.46174131, 'kernel': 'rbf', 'gamma': 3.3235330357747726e-07}
0.739 (+/-0.437) for {'C': 120226443.46174131, 'kernel': 'rbf', 'gamma': 3.3357999666204803e-07}
0.744 (+/-0.433) for {'C': 120226443.46174131, 'kernel': 'rbf', 'gamma': 3.3481121738605415e-07}
0.694 (+/-0.355) for {'C': 120226443.46174131, 'kernel': 'rbf', 'gamma': 3.3604698246069916e-07}
0.706 (+/-0.418) for {'C': 120226443.46174131, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.702 (+/-0.405) for {'C': 131825673.85564049, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.687 (+/-0.424) for {'C': 131825673.85564049, 'kernel': 'rbf', 'gamma': 3.2628717214308483e-07}
0.689 (+/-0.422) for {'C': 131825673.85564049, 'kernel': 'rbf', 'gamma': 3.2749147555557826e-07}
0.706 (+/-0.403) for {'C': 131825673.85564049, 'kernel': 'rbf', 'gamma': 3.287002239687748e-07}
0.739 (+/-0.437) for {'C': 131825673.85564049, 'kernel': 'rbf', 'gamma': 3.299134337888643e-07}
0.739 (+/-0.437) for {'C': 131825673.85564049, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.739 (+/-0.437) for {'C': 131825673.85564049, 'kernel': 'rbf', 'gamma': 3.3235330357747726e-07}
0.689 (+/-0.421) for {'C': 131825673.85564049, 'kernel': 'rbf', 'gamma': 3.3357999666204803e-07}
0.743 (+/-0.433) for {'C': 131825673.85564049, 'kernel': 'rbf', 'gamma': 3.3481121738605415e-07}
0.694 (+/-0.355) for {'C': 131825673.85564049, 'kernel': 'rbf', 'gamma': 3.3604698246069916e-07}
0.706 (+/-0.418) for {'C': 131825673.85564049, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.689 (+/-0.422) for {'C': 144543977.0745926, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.687 (+/-0.424) for {'C': 144543977.0745926, 'kernel': 'rbf', 'gamma': 3.2628717214308483e-07}
0.689 (+/-0.422) for {'C': 144543977.0745926, 'kernel': 'rbf', 'gamma': 3.2749147555557826e-07}
0.706 (+/-0.403) for {'C': 144543977.0745926, 'kernel': 'rbf', 'gamma': 3.287002239687748e-07}
0.739 (+/-0.437) for {'C': 144543977.0745926, 'kernel': 'rbf', 'gamma': 3.299134337888643e-07}
0.739 (+/-0.437) for {'C': 144543977.0745926, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.739 (+/-0.437) for {'C': 144543977.0745926, 'kernel': 'rbf', 'gamma': 3.3235330357747726e-07}
0.689 (+/-0.421) for {'C': 144543977.0745926, 'kernel': 'rbf', 'gamma': 3.3357999666204803e-07}
0.744 (+/-0.433) for {'C': 144543977.0745926, 'kernel': 'rbf', 'gamma': 3.3481121738605415e-07}
0.694 (+/-0.355) for {'C': 144543977.0745926, 'kernel': 'rbf', 'gamma': 3.3604698246069916e-07}
0.706 (+/-0.418) for {'C': 144543977.0745926, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.702 (+/-0.405) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.687 (+/-0.424) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.2628717214308483e-07}
0.689 (+/-0.422) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.2749147555557826e-07}
0.706 (+/-0.403) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.287002239687748e-07}
0.739 (+/-0.437) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.299134337888643e-07}
0.739 (+/-0.437) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.739 (+/-0.437) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.3235330357747726e-07}
0.689 (+/-0.421) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.3357999666204803e-07}
0.743 (+/-0.433) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.3481121738605415e-07}
0.694 (+/-0.355) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.3604698246069916e-07}
0.706 (+/-0.418) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.702 (+/-0.405) for {'C': 173780082.87493756, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.687 (+/-0.424) for {'C': 173780082.87493756, 'kernel': 'rbf', 'gamma': 3.2628717214308483e-07}
0.689 (+/-0.422) for {'C': 173780082.87493756, 'kernel': 'rbf', 'gamma': 3.2749147555557826e-07}
0.706 (+/-0.403) for {'C': 173780082.87493756, 'kernel': 'rbf', 'gamma': 3.287002239687748e-07}
0.739 (+/-0.437) for {'C': 173780082.87493756, 'kernel': 'rbf', 'gamma': 3.299134337888643e-07}
0.739 (+/-0.437) for {'C': 173780082.87493756, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.739 (+/-0.437) for {'C': 173780082.87493756, 'kernel': 'rbf', 'gamma': 3.3235330357747726e-07}
0.693 (+/-0.419) for {'C': 173780082.87493756, 'kernel': 'rbf', 'gamma': 3.3357999666204803e-07}
0.744 (+/-0.433) for {'C': 173780082.87493756, 'kernel': 'rbf', 'gamma': 3.3481121738605415e-07}
0.694 (+/-0.355) for {'C': 173780082.87493756, 'kernel': 'rbf', 'gamma': 3.3604698246069916e-07}
0.706 (+/-0.418) for {'C': 173780082.87493756, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.702 (+/-0.405) for {'C': 190546071.79632485, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.689 (+/-0.422) for {'C': 190546071.79632485, 'kernel': 'rbf', 'gamma': 3.2628717214308483e-07}
0.689 (+/-0.422) for {'C': 190546071.79632485, 'kernel': 'rbf', 'gamma': 3.2749147555557826e-07}
0.706 (+/-0.403) for {'C': 190546071.79632485, 'kernel': 'rbf', 'gamma': 3.287002239687748e-07}
0.739 (+/-0.437) for {'C': 190546071.79632485, 'kernel': 'rbf', 'gamma': 3.299134337888643e-07}
0.739 (+/-0.437) for {'C': 190546071.79632485, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.739 (+/-0.437) for {'C': 190546071.79632485, 'kernel': 'rbf', 'gamma': 3.3235330357747726e-07}
0.693 (+/-0.419) for {'C': 190546071.79632485, 'kernel': 'rbf', 'gamma': 3.3357999666204803e-07}
0.744 (+/-0.433) for {'C': 190546071.79632485, 'kernel': 'rbf', 'gamma': 3.3481121738605415e-07}
0.694 (+/-0.355) for {'C': 190546071.79632485, 'kernel': 'rbf', 'gamma': 3.3604698246069916e-07}
0.706 (+/-0.418) for {'C': 190546071.79632485, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.702 (+/-0.405) for {'C': 208929613.08540368, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.689 (+/-0.422) for {'C': 208929613.08540368, 'kernel': 'rbf', 'gamma': 3.2628717214308483e-07}
0.689 (+/-0.422) for {'C': 208929613.08540368, 'kernel': 'rbf', 'gamma': 3.2749147555557826e-07}
0.706 (+/-0.403) for {'C': 208929613.08540368, 'kernel': 'rbf', 'gamma': 3.287002239687748e-07}
0.739 (+/-0.437) for {'C': 208929613.08540368, 'kernel': 'rbf', 'gamma': 3.299134337888643e-07}
0.739 (+/-0.437) for {'C': 208929613.08540368, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.739 (+/-0.437) for {'C': 208929613.08540368, 'kernel': 'rbf', 'gamma': 3.3235330357747726e-07}
0.693 (+/-0.419) for {'C': 208929613.08540368, 'kernel': 'rbf', 'gamma': 3.3357999666204803e-07}
0.744 (+/-0.433) for {'C': 208929613.08540368, 'kernel': 'rbf', 'gamma': 3.3481121738605415e-07}
0.694 (+/-0.355) for {'C': 208929613.08540368, 'kernel': 'rbf', 'gamma': 3.3604698246069916e-07}
0.706 (+/-0.418) for {'C': 208929613.08540368, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.702 (+/-0.405) for {'C': 229086765.27677715, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.689 (+/-0.422) for {'C': 229086765.27677715, 'kernel': 'rbf', 'gamma': 3.2628717214308483e-07}
0.689 (+/-0.422) for {'C': 229086765.27677715, 'kernel': 'rbf', 'gamma': 3.2749147555557826e-07}
0.706 (+/-0.403) for {'C': 229086765.27677715, 'kernel': 'rbf', 'gamma': 3.287002239687748e-07}
0.739 (+/-0.437) for {'C': 229086765.27677715, 'kernel': 'rbf', 'gamma': 3.299134337888643e-07}
0.739 (+/-0.437) for {'C': 229086765.27677715, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.739 (+/-0.437) for {'C': 229086765.27677715, 'kernel': 'rbf', 'gamma': 3.3235330357747726e-07}
0.743 (+/-0.433) for {'C': 229086765.27677715, 'kernel': 'rbf', 'gamma': 3.3357999666204803e-07}
0.744 (+/-0.433) for {'C': 229086765.27677715, 'kernel': 'rbf', 'gamma': 3.3481121738605415e-07}
0.694 (+/-0.355) for {'C': 229086765.27677715, 'kernel': 'rbf', 'gamma': 3.3604698246069916e-07}
0.706 (+/-0.418) for {'C': 229086765.27677715, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.702 (+/-0.405) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.689 (+/-0.422) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.2628717214308483e-07}
0.689 (+/-0.422) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.2749147555557826e-07}
0.706 (+/-0.403) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.287002239687748e-07}
0.739 (+/-0.437) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.299134337888643e-07}
0.739 (+/-0.437) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.739 (+/-0.437) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.3235330357747726e-07}
0.689 (+/-0.421) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.3357999666204803e-07}
0.743 (+/-0.433) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.3481121738605415e-07}
0.719 (+/-0.399) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.3604698246069916e-07}
0.706 (+/-0.418) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'linear'}
0.706 (+/-0.383) for {'C': 10.0, 'kernel': 'linear'}
0.635 (+/-0.313) for {'C': 100.0, 'kernel': 'linear'}
0.632 (+/-0.309) for {'C': 1000.0, 'kernel': 'linear'}
0.629 (+/-0.312) for {'C': 10000.0, 'kernel': 'linear'}
0.624 (+/-0.319) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.665 (+/-0.316) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.665 (+/-0.316) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.2628717214308483e-07}
0.665 (+/-0.316) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.2749147555557826e-07}
0.682 (+/-0.295) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.287002239687748e-07}
0.682 (+/-0.296) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.299134337888643e-07}
0.682 (+/-0.296) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.657 (+/-0.216) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.3235330357747726e-07}
0.632 (+/-0.224) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.3357999666204803e-07}
0.682 (+/-0.296) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.3481121738605415e-07}
0.682 (+/-0.296) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.3604698246069916e-07}
0.665 (+/-0.316) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.665 (+/-0.316) for {'C': 109647819.61431846, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.665 (+/-0.316) for {'C': 109647819.61431846, 'kernel': 'rbf', 'gamma': 3.2628717214308483e-07}
0.665 (+/-0.316) for {'C': 109647819.61431846, 'kernel': 'rbf', 'gamma': 3.2749147555557826e-07}
0.682 (+/-0.296) for {'C': 109647819.61431846, 'kernel': 'rbf', 'gamma': 3.287002239687748e-07}
0.682 (+/-0.296) for {'C': 109647819.61431846, 'kernel': 'rbf', 'gamma': 3.299134337888643e-07}
0.682 (+/-0.296) for {'C': 109647819.61431846, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.657 (+/-0.216) for {'C': 109647819.61431846, 'kernel': 'rbf', 'gamma': 3.3235330357747726e-07}
0.632 (+/-0.224) for {'C': 109647819.61431846, 'kernel': 'rbf', 'gamma': 3.3357999666204803e-07}
0.657 (+/-0.216) for {'C': 109647819.61431846, 'kernel': 'rbf', 'gamma': 3.3481121738605415e-07}
0.682 (+/-0.296) for {'C': 109647819.61431846, 'kernel': 'rbf', 'gamma': 3.3604698246069916e-07}
0.665 (+/-0.316) for {'C': 109647819.61431846, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.681 (+/-0.296) for {'C': 120226443.46174131, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.665 (+/-0.316) for {'C': 120226443.46174131, 'kernel': 'rbf', 'gamma': 3.2628717214308483e-07}
0.665 (+/-0.316) for {'C': 120226443.46174131, 'kernel': 'rbf', 'gamma': 3.2749147555557826e-07}
0.682 (+/-0.296) for {'C': 120226443.46174131, 'kernel': 'rbf', 'gamma': 3.287002239687748e-07}
0.682 (+/-0.296) for {'C': 120226443.46174131, 'kernel': 'rbf', 'gamma': 3.299134337888643e-07}
0.682 (+/-0.296) for {'C': 120226443.46174131, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.657 (+/-0.216) for {'C': 120226443.46174131, 'kernel': 'rbf', 'gamma': 3.3235330357747726e-07}
0.657 (+/-0.215) for {'C': 120226443.46174131, 'kernel': 'rbf', 'gamma': 3.3357999666204803e-07}
0.682 (+/-0.296) for {'C': 120226443.46174131, 'kernel': 'rbf', 'gamma': 3.3481121738605415e-07}
0.682 (+/-0.296) for {'C': 120226443.46174131, 'kernel': 'rbf', 'gamma': 3.3604698246069916e-07}
0.665 (+/-0.316) for {'C': 120226443.46174131, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.681 (+/-0.296) for {'C': 131825673.85564049, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.665 (+/-0.316) for {'C': 131825673.85564049, 'kernel': 'rbf', 'gamma': 3.2628717214308483e-07}
0.665 (+/-0.316) for {'C': 131825673.85564049, 'kernel': 'rbf', 'gamma': 3.2749147555557826e-07}
0.682 (+/-0.296) for {'C': 131825673.85564049, 'kernel': 'rbf', 'gamma': 3.287002239687748e-07}
0.682 (+/-0.296) for {'C': 131825673.85564049, 'kernel': 'rbf', 'gamma': 3.299134337888643e-07}
0.682 (+/-0.296) for {'C': 131825673.85564049, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.657 (+/-0.215) for {'C': 131825673.85564049, 'kernel': 'rbf', 'gamma': 3.3235330357747726e-07}
0.632 (+/-0.224) for {'C': 131825673.85564049, 'kernel': 'rbf', 'gamma': 3.3357999666204803e-07}
0.657 (+/-0.216) for {'C': 131825673.85564049, 'kernel': 'rbf', 'gamma': 3.3481121738605415e-07}
0.682 (+/-0.296) for {'C': 131825673.85564049, 'kernel': 'rbf', 'gamma': 3.3604698246069916e-07}
0.665 (+/-0.316) for {'C': 131825673.85564049, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.665 (+/-0.316) for {'C': 144543977.0745926, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.665 (+/-0.316) for {'C': 144543977.0745926, 'kernel': 'rbf', 'gamma': 3.2628717214308483e-07}
0.665 (+/-0.316) for {'C': 144543977.0745926, 'kernel': 'rbf', 'gamma': 3.2749147555557826e-07}
0.682 (+/-0.295) for {'C': 144543977.0745926, 'kernel': 'rbf', 'gamma': 3.287002239687748e-07}
0.682 (+/-0.296) for {'C': 144543977.0745926, 'kernel': 'rbf', 'gamma': 3.299134337888643e-07}
0.682 (+/-0.296) for {'C': 144543977.0745926, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.657 (+/-0.215) for {'C': 144543977.0745926, 'kernel': 'rbf', 'gamma': 3.3235330357747726e-07}
0.632 (+/-0.224) for {'C': 144543977.0745926, 'kernel': 'rbf', 'gamma': 3.3357999666204803e-07}
0.682 (+/-0.296) for {'C': 144543977.0745926, 'kernel': 'rbf', 'gamma': 3.3481121738605415e-07}
0.682 (+/-0.296) for {'C': 144543977.0745926, 'kernel': 'rbf', 'gamma': 3.3604698246069916e-07}
0.665 (+/-0.316) for {'C': 144543977.0745926, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.681 (+/-0.296) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.665 (+/-0.316) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.2628717214308483e-07}
0.665 (+/-0.316) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.2749147555557826e-07}
0.682 (+/-0.296) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.287002239687748e-07}
0.682 (+/-0.296) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.299134337888643e-07}
0.682 (+/-0.296) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.657 (+/-0.216) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.3235330357747726e-07}
0.632 (+/-0.224) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.3357999666204803e-07}
0.657 (+/-0.216) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.3481121738605415e-07}
0.682 (+/-0.296) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.3604698246069916e-07}
0.665 (+/-0.316) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.681 (+/-0.296) for {'C': 173780082.87493756, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.665 (+/-0.316) for {'C': 173780082.87493756, 'kernel': 'rbf', 'gamma': 3.2628717214308483e-07}
0.665 (+/-0.316) for {'C': 173780082.87493756, 'kernel': 'rbf', 'gamma': 3.2749147555557826e-07}
0.682 (+/-0.295) for {'C': 173780082.87493756, 'kernel': 'rbf', 'gamma': 3.287002239687748e-07}
0.682 (+/-0.296) for {'C': 173780082.87493756, 'kernel': 'rbf', 'gamma': 3.299134337888643e-07}
0.682 (+/-0.296) for {'C': 173780082.87493756, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.657 (+/-0.215) for {'C': 173780082.87493756, 'kernel': 'rbf', 'gamma': 3.3235330357747726e-07}
0.632 (+/-0.224) for {'C': 173780082.87493756, 'kernel': 'rbf', 'gamma': 3.3357999666204803e-07}
0.682 (+/-0.296) for {'C': 173780082.87493756, 'kernel': 'rbf', 'gamma': 3.3481121738605415e-07}
0.682 (+/-0.296) for {'C': 173780082.87493756, 'kernel': 'rbf', 'gamma': 3.3604698246069916e-07}
0.665 (+/-0.316) for {'C': 173780082.87493756, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.681 (+/-0.296) for {'C': 190546071.79632485, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.665 (+/-0.316) for {'C': 190546071.79632485, 'kernel': 'rbf', 'gamma': 3.2628717214308483e-07}
0.665 (+/-0.316) for {'C': 190546071.79632485, 'kernel': 'rbf', 'gamma': 3.2749147555557826e-07}
0.682 (+/-0.295) for {'C': 190546071.79632485, 'kernel': 'rbf', 'gamma': 3.287002239687748e-07}
0.682 (+/-0.296) for {'C': 190546071.79632485, 'kernel': 'rbf', 'gamma': 3.299134337888643e-07}
0.682 (+/-0.296) for {'C': 190546071.79632485, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.657 (+/-0.215) for {'C': 190546071.79632485, 'kernel': 'rbf', 'gamma': 3.3235330357747726e-07}
0.632 (+/-0.224) for {'C': 190546071.79632485, 'kernel': 'rbf', 'gamma': 3.3357999666204803e-07}
0.682 (+/-0.296) for {'C': 190546071.79632485, 'kernel': 'rbf', 'gamma': 3.3481121738605415e-07}
0.682 (+/-0.296) for {'C': 190546071.79632485, 'kernel': 'rbf', 'gamma': 3.3604698246069916e-07}
0.665 (+/-0.316) for {'C': 190546071.79632485, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.681 (+/-0.296) for {'C': 208929613.08540368, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.665 (+/-0.316) for {'C': 208929613.08540368, 'kernel': 'rbf', 'gamma': 3.2628717214308483e-07}
0.665 (+/-0.316) for {'C': 208929613.08540368, 'kernel': 'rbf', 'gamma': 3.2749147555557826e-07}
0.682 (+/-0.296) for {'C': 208929613.08540368, 'kernel': 'rbf', 'gamma': 3.287002239687748e-07}
0.682 (+/-0.296) for {'C': 208929613.08540368, 'kernel': 'rbf', 'gamma': 3.299134337888643e-07}
0.682 (+/-0.296) for {'C': 208929613.08540368, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.657 (+/-0.216) for {'C': 208929613.08540368, 'kernel': 'rbf', 'gamma': 3.3235330357747726e-07}
0.632 (+/-0.224) for {'C': 208929613.08540368, 'kernel': 'rbf', 'gamma': 3.3357999666204803e-07}
0.682 (+/-0.296) for {'C': 208929613.08540368, 'kernel': 'rbf', 'gamma': 3.3481121738605415e-07}
0.682 (+/-0.296) for {'C': 208929613.08540368, 'kernel': 'rbf', 'gamma': 3.3604698246069916e-07}
0.665 (+/-0.316) for {'C': 208929613.08540368, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.681 (+/-0.296) for {'C': 229086765.27677715, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.665 (+/-0.316) for {'C': 229086765.27677715, 'kernel': 'rbf', 'gamma': 3.2628717214308483e-07}
0.665 (+/-0.316) for {'C': 229086765.27677715, 'kernel': 'rbf', 'gamma': 3.2749147555557826e-07}
0.682 (+/-0.296) for {'C': 229086765.27677715, 'kernel': 'rbf', 'gamma': 3.287002239687748e-07}
0.682 (+/-0.296) for {'C': 229086765.27677715, 'kernel': 'rbf', 'gamma': 3.299134337888643e-07}
0.682 (+/-0.296) for {'C': 229086765.27677715, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.657 (+/-0.216) for {'C': 229086765.27677715, 'kernel': 'rbf', 'gamma': 3.3235330357747726e-07}
0.657 (+/-0.216) for {'C': 229086765.27677715, 'kernel': 'rbf', 'gamma': 3.3357999666204803e-07}
0.682 (+/-0.296) for {'C': 229086765.27677715, 'kernel': 'rbf', 'gamma': 3.3481121738605415e-07}
0.682 (+/-0.296) for {'C': 229086765.27677715, 'kernel': 'rbf', 'gamma': 3.3604698246069916e-07}
0.665 (+/-0.316) for {'C': 229086765.27677715, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.681 (+/-0.296) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.665 (+/-0.316) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.2628717214308483e-07}
0.665 (+/-0.316) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.2749147555557826e-07}
0.682 (+/-0.296) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.287002239687748e-07}
0.682 (+/-0.296) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.299134337888643e-07}
0.682 (+/-0.296) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.657 (+/-0.216) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.3235330357747726e-07}
0.632 (+/-0.224) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.3357999666204803e-07}
0.657 (+/-0.216) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.3481121738605415e-07}
0.682 (+/-0.296) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.3604698246069916e-07}
0.665 (+/-0.316) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 10.0, 'kernel': 'linear'}
0.636 (+/-0.237) for {'C': 100.0, 'kernel': 'linear'}
0.637 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
0.636 (+/-0.240) for {'C': 10000.0, 'kernel': 'linear'}
0.610 (+/-0.245) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.3481121738605415e-07}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6822099925797676
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.620 (+/-0.309) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.629 (+/-0.326) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.2628717214308483e-07}
0.622 (+/-0.308) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.2749147555557826e-07}
0.638 (+/-0.319) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.287002239687748e-07}
0.631 (+/-0.302) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.299134337888643e-07}
0.625 (+/-0.305) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.629 (+/-0.303) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.3235330357747726e-07}
0.621 (+/-0.294) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.3357999666204803e-07}
0.605 (+/-0.301) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.3481121738605415e-07}
0.620 (+/-0.290) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.3604698246069916e-07}
0.617 (+/-0.292) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.620 (+/-0.309) for {'C': 109647819.61431846, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.621 (+/-0.308) for {'C': 109647819.61431846, 'kernel': 'rbf', 'gamma': 3.2628717214308483e-07}
0.622 (+/-0.308) for {'C': 109647819.61431846, 'kernel': 'rbf', 'gamma': 3.2749147555557826e-07}
0.638 (+/-0.319) for {'C': 109647819.61431846, 'kernel': 'rbf', 'gamma': 3.287002239687748e-07}
0.631 (+/-0.302) for {'C': 109647819.61431846, 'kernel': 'rbf', 'gamma': 3.299134337888643e-07}
0.625 (+/-0.305) for {'C': 109647819.61431846, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.626 (+/-0.305) for {'C': 109647819.61431846, 'kernel': 'rbf', 'gamma': 3.3235330357747726e-07}
0.620 (+/-0.294) for {'C': 109647819.61431846, 'kernel': 'rbf', 'gamma': 3.3357999666204803e-07}
0.605 (+/-0.301) for {'C': 109647819.61431846, 'kernel': 'rbf', 'gamma': 3.3481121738605415e-07}
0.620 (+/-0.290) for {'C': 109647819.61431846, 'kernel': 'rbf', 'gamma': 3.3604698246069916e-07}
0.617 (+/-0.292) for {'C': 109647819.61431846, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.620 (+/-0.310) for {'C': 120226443.46174131, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.621 (+/-0.308) for {'C': 120226443.46174131, 'kernel': 'rbf', 'gamma': 3.2628717214308483e-07}
0.622 (+/-0.308) for {'C': 120226443.46174131, 'kernel': 'rbf', 'gamma': 3.2749147555557826e-07}
0.638 (+/-0.319) for {'C': 120226443.46174131, 'kernel': 'rbf', 'gamma': 3.287002239687748e-07}
0.631 (+/-0.302) for {'C': 120226443.46174131, 'kernel': 'rbf', 'gamma': 3.299134337888643e-07}
0.625 (+/-0.305) for {'C': 120226443.46174131, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.626 (+/-0.305) for {'C': 120226443.46174131, 'kernel': 'rbf', 'gamma': 3.3235330357747726e-07}
0.620 (+/-0.294) for {'C': 120226443.46174131, 'kernel': 'rbf', 'gamma': 3.3357999666204803e-07}
0.605 (+/-0.301) for {'C': 120226443.46174131, 'kernel': 'rbf', 'gamma': 3.3481121738605415e-07}
0.620 (+/-0.290) for {'C': 120226443.46174131, 'kernel': 'rbf', 'gamma': 3.3604698246069916e-07}
0.617 (+/-0.292) for {'C': 120226443.46174131, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.620 (+/-0.309) for {'C': 131825673.85564049, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.621 (+/-0.308) for {'C': 131825673.85564049, 'kernel': 'rbf', 'gamma': 3.2628717214308483e-07}
0.622 (+/-0.308) for {'C': 131825673.85564049, 'kernel': 'rbf', 'gamma': 3.2749147555557826e-07}
0.629 (+/-0.302) for {'C': 131825673.85564049, 'kernel': 'rbf', 'gamma': 3.287002239687748e-07}
0.631 (+/-0.302) for {'C': 131825673.85564049, 'kernel': 'rbf', 'gamma': 3.299134337888643e-07}
0.625 (+/-0.305) for {'C': 131825673.85564049, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.626 (+/-0.305) for {'C': 131825673.85564049, 'kernel': 'rbf', 'gamma': 3.3235330357747726e-07}
0.620 (+/-0.294) for {'C': 131825673.85564049, 'kernel': 'rbf', 'gamma': 3.3357999666204803e-07}
0.605 (+/-0.301) for {'C': 131825673.85564049, 'kernel': 'rbf', 'gamma': 3.3481121738605415e-07}
0.622 (+/-0.290) for {'C': 131825673.85564049, 'kernel': 'rbf', 'gamma': 3.3604698246069916e-07}
0.617 (+/-0.292) for {'C': 131825673.85564049, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.620 (+/-0.310) for {'C': 144543977.0745926, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.621 (+/-0.308) for {'C': 144543977.0745926, 'kernel': 'rbf', 'gamma': 3.2628717214308483e-07}
0.622 (+/-0.308) for {'C': 144543977.0745926, 'kernel': 'rbf', 'gamma': 3.2749147555557826e-07}
0.629 (+/-0.302) for {'C': 144543977.0745926, 'kernel': 'rbf', 'gamma': 3.287002239687748e-07}
0.631 (+/-0.302) for {'C': 144543977.0745926, 'kernel': 'rbf', 'gamma': 3.299134337888643e-07}
0.625 (+/-0.305) for {'C': 144543977.0745926, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.626 (+/-0.305) for {'C': 144543977.0745926, 'kernel': 'rbf', 'gamma': 3.3235330357747726e-07}
0.620 (+/-0.294) for {'C': 144543977.0745926, 'kernel': 'rbf', 'gamma': 3.3357999666204803e-07}
0.605 (+/-0.301) for {'C': 144543977.0745926, 'kernel': 'rbf', 'gamma': 3.3481121738605415e-07}
0.620 (+/-0.290) for {'C': 144543977.0745926, 'kernel': 'rbf', 'gamma': 3.3604698246069916e-07}
0.617 (+/-0.292) for {'C': 144543977.0745926, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.620 (+/-0.310) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.621 (+/-0.308) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.2628717214308483e-07}
0.622 (+/-0.308) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.2749147555557826e-07}
0.629 (+/-0.302) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.287002239687748e-07}
0.631 (+/-0.302) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.299134337888643e-07}
0.630 (+/-0.303) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.627 (+/-0.304) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.3235330357747726e-07}
0.620 (+/-0.294) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.3357999666204803e-07}
0.605 (+/-0.301) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.3481121738605415e-07}
0.620 (+/-0.290) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.3604698246069916e-07}
0.617 (+/-0.292) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.620 (+/-0.310) for {'C': 173780082.87493756, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.621 (+/-0.308) for {'C': 173780082.87493756, 'kernel': 'rbf', 'gamma': 3.2628717214308483e-07}
0.622 (+/-0.308) for {'C': 173780082.87493756, 'kernel': 'rbf', 'gamma': 3.2749147555557826e-07}
0.638 (+/-0.319) for {'C': 173780082.87493756, 'kernel': 'rbf', 'gamma': 3.287002239687748e-07}
0.631 (+/-0.302) for {'C': 173780082.87493756, 'kernel': 'rbf', 'gamma': 3.299134337888643e-07}
0.625 (+/-0.306) for {'C': 173780082.87493756, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.626 (+/-0.305) for {'C': 173780082.87493756, 'kernel': 'rbf', 'gamma': 3.3235330357747726e-07}
0.620 (+/-0.294) for {'C': 173780082.87493756, 'kernel': 'rbf', 'gamma': 3.3357999666204803e-07}
0.605 (+/-0.301) for {'C': 173780082.87493756, 'kernel': 'rbf', 'gamma': 3.3481121738605415e-07}
0.618 (+/-0.290) for {'C': 173780082.87493756, 'kernel': 'rbf', 'gamma': 3.3604698246069916e-07}
0.617 (+/-0.292) for {'C': 173780082.87493756, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.620 (+/-0.310) for {'C': 190546071.79632485, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.620 (+/-0.309) for {'C': 190546071.79632485, 'kernel': 'rbf', 'gamma': 3.2628717214308483e-07}
0.622 (+/-0.308) for {'C': 190546071.79632485, 'kernel': 'rbf', 'gamma': 3.2749147555557826e-07}
0.638 (+/-0.319) for {'C': 190546071.79632485, 'kernel': 'rbf', 'gamma': 3.287002239687748e-07}
0.631 (+/-0.302) for {'C': 190546071.79632485, 'kernel': 'rbf', 'gamma': 3.299134337888643e-07}
0.625 (+/-0.306) for {'C': 190546071.79632485, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.626 (+/-0.305) for {'C': 190546071.79632485, 'kernel': 'rbf', 'gamma': 3.3235330357747726e-07}
0.620 (+/-0.294) for {'C': 190546071.79632485, 'kernel': 'rbf', 'gamma': 3.3357999666204803e-07}
0.614 (+/-0.293) for {'C': 190546071.79632485, 'kernel': 'rbf', 'gamma': 3.3481121738605415e-07}
0.618 (+/-0.290) for {'C': 190546071.79632485, 'kernel': 'rbf', 'gamma': 3.3604698246069916e-07}
0.617 (+/-0.292) for {'C': 190546071.79632485, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.619 (+/-0.310) for {'C': 208929613.08540368, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.620 (+/-0.309) for {'C': 208929613.08540368, 'kernel': 'rbf', 'gamma': 3.2628717214308483e-07}
0.622 (+/-0.308) for {'C': 208929613.08540368, 'kernel': 'rbf', 'gamma': 3.2749147555557826e-07}
0.629 (+/-0.302) for {'C': 208929613.08540368, 'kernel': 'rbf', 'gamma': 3.287002239687748e-07}
0.631 (+/-0.302) for {'C': 208929613.08540368, 'kernel': 'rbf', 'gamma': 3.299134337888643e-07}
0.625 (+/-0.306) for {'C': 208929613.08540368, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.626 (+/-0.305) for {'C': 208929613.08540368, 'kernel': 'rbf', 'gamma': 3.3235330357747726e-07}
0.620 (+/-0.294) for {'C': 208929613.08540368, 'kernel': 'rbf', 'gamma': 3.3357999666204803e-07}
0.614 (+/-0.293) for {'C': 208929613.08540368, 'kernel': 'rbf', 'gamma': 3.3481121738605415e-07}
0.618 (+/-0.290) for {'C': 208929613.08540368, 'kernel': 'rbf', 'gamma': 3.3604698246069916e-07}
0.617 (+/-0.292) for {'C': 208929613.08540368, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.619 (+/-0.310) for {'C': 229086765.27677715, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.620 (+/-0.309) for {'C': 229086765.27677715, 'kernel': 'rbf', 'gamma': 3.2628717214308483e-07}
0.622 (+/-0.308) for {'C': 229086765.27677715, 'kernel': 'rbf', 'gamma': 3.2749147555557826e-07}
0.638 (+/-0.319) for {'C': 229086765.27677715, 'kernel': 'rbf', 'gamma': 3.287002239687748e-07}
0.631 (+/-0.302) for {'C': 229086765.27677715, 'kernel': 'rbf', 'gamma': 3.299134337888643e-07}
0.625 (+/-0.306) for {'C': 229086765.27677715, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.626 (+/-0.305) for {'C': 229086765.27677715, 'kernel': 'rbf', 'gamma': 3.3235330357747726e-07}
0.620 (+/-0.294) for {'C': 229086765.27677715, 'kernel': 'rbf', 'gamma': 3.3357999666204803e-07}
0.614 (+/-0.293) for {'C': 229086765.27677715, 'kernel': 'rbf', 'gamma': 3.3481121738605415e-07}
0.618 (+/-0.290) for {'C': 229086765.27677715, 'kernel': 'rbf', 'gamma': 3.3604698246069916e-07}
0.617 (+/-0.292) for {'C': 229086765.27677715, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.619 (+/-0.310) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.620 (+/-0.309) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.2628717214308483e-07}
0.622 (+/-0.308) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.2749147555557826e-07}
0.629 (+/-0.302) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.287002239687748e-07}
0.631 (+/-0.302) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.299134337888643e-07}
0.630 (+/-0.303) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.627 (+/-0.304) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.3235330357747726e-07}
0.620 (+/-0.294) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.3357999666204803e-07}
0.614 (+/-0.293) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.3481121738605415e-07}
0.618 (+/-0.290) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.3604698246069916e-07}
0.617 (+/-0.292) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.748 (+/-0.449) for {'C': 1.0, 'kernel': 'linear'}
0.666 (+/-0.379) for {'C': 10.0, 'kernel': 'linear'}
0.629 (+/-0.313) for {'C': 100.0, 'kernel': 'linear'}
0.632 (+/-0.309) for {'C': 1000.0, 'kernel': 'linear'}
0.629 (+/-0.312) for {'C': 10000.0, 'kernel': 'linear'}
0.624 (+/-0.319) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.20 0.17 0.18 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.677 (+/-0.292) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.678 (+/-0.293) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.2628717214308483e-07}
0.678 (+/-0.292) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.2749147555557826e-07}
0.679 (+/-0.293) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.287002239687748e-07}
0.679 (+/-0.293) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.299134337888643e-07}
0.678 (+/-0.293) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.679 (+/-0.292) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.3235330357747726e-07}
0.679 (+/-0.291) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.3357999666204803e-07}
0.661 (+/-0.311) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.3481121738605415e-07}
0.679 (+/-0.289) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.3604698246069916e-07}
0.678 (+/-0.285) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.677 (+/-0.292) for {'C': 109647819.61431846, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.678 (+/-0.292) for {'C': 109647819.61431846, 'kernel': 'rbf', 'gamma': 3.2628717214308483e-07}
0.678 (+/-0.292) for {'C': 109647819.61431846, 'kernel': 'rbf', 'gamma': 3.2749147555557826e-07}
0.679 (+/-0.293) for {'C': 109647819.61431846, 'kernel': 'rbf', 'gamma': 3.287002239687748e-07}
0.679 (+/-0.293) for {'C': 109647819.61431846, 'kernel': 'rbf', 'gamma': 3.299134337888643e-07}
0.678 (+/-0.293) for {'C': 109647819.61431846, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.678 (+/-0.293) for {'C': 109647819.61431846, 'kernel': 'rbf', 'gamma': 3.3235330357747726e-07}
0.678 (+/-0.291) for {'C': 109647819.61431846, 'kernel': 'rbf', 'gamma': 3.3357999666204803e-07}
0.661 (+/-0.311) for {'C': 109647819.61431846, 'kernel': 'rbf', 'gamma': 3.3481121738605415e-07}
0.679 (+/-0.289) for {'C': 109647819.61431846, 'kernel': 'rbf', 'gamma': 3.3604698246069916e-07}
0.678 (+/-0.285) for {'C': 109647819.61431846, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.677 (+/-0.292) for {'C': 120226443.46174131, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.678 (+/-0.292) for {'C': 120226443.46174131, 'kernel': 'rbf', 'gamma': 3.2628717214308483e-07}
0.678 (+/-0.292) for {'C': 120226443.46174131, 'kernel': 'rbf', 'gamma': 3.2749147555557826e-07}
0.679 (+/-0.293) for {'C': 120226443.46174131, 'kernel': 'rbf', 'gamma': 3.287002239687748e-07}
0.679 (+/-0.293) for {'C': 120226443.46174131, 'kernel': 'rbf', 'gamma': 3.299134337888643e-07}
0.678 (+/-0.293) for {'C': 120226443.46174131, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.678 (+/-0.293) for {'C': 120226443.46174131, 'kernel': 'rbf', 'gamma': 3.3235330357747726e-07}
0.678 (+/-0.291) for {'C': 120226443.46174131, 'kernel': 'rbf', 'gamma': 3.3357999666204803e-07}
0.661 (+/-0.311) for {'C': 120226443.46174131, 'kernel': 'rbf', 'gamma': 3.3481121738605415e-07}
0.679 (+/-0.289) for {'C': 120226443.46174131, 'kernel': 'rbf', 'gamma': 3.3604698246069916e-07}
0.678 (+/-0.285) for {'C': 120226443.46174131, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.677 (+/-0.292) for {'C': 131825673.85564049, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.678 (+/-0.292) for {'C': 131825673.85564049, 'kernel': 'rbf', 'gamma': 3.2628717214308483e-07}
0.678 (+/-0.292) for {'C': 131825673.85564049, 'kernel': 'rbf', 'gamma': 3.2749147555557826e-07}
0.679 (+/-0.293) for {'C': 131825673.85564049, 'kernel': 'rbf', 'gamma': 3.287002239687748e-07}
0.679 (+/-0.293) for {'C': 131825673.85564049, 'kernel': 'rbf', 'gamma': 3.299134337888643e-07}
0.678 (+/-0.293) for {'C': 131825673.85564049, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.678 (+/-0.293) for {'C': 131825673.85564049, 'kernel': 'rbf', 'gamma': 3.3235330357747726e-07}
0.678 (+/-0.291) for {'C': 131825673.85564049, 'kernel': 'rbf', 'gamma': 3.3357999666204803e-07}
0.661 (+/-0.311) for {'C': 131825673.85564049, 'kernel': 'rbf', 'gamma': 3.3481121738605415e-07}
0.679 (+/-0.290) for {'C': 131825673.85564049, 'kernel': 'rbf', 'gamma': 3.3604698246069916e-07}
0.678 (+/-0.285) for {'C': 131825673.85564049, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.677 (+/-0.292) for {'C': 144543977.0745926, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.678 (+/-0.292) for {'C': 144543977.0745926, 'kernel': 'rbf', 'gamma': 3.2628717214308483e-07}
0.678 (+/-0.292) for {'C': 144543977.0745926, 'kernel': 'rbf', 'gamma': 3.2749147555557826e-07}
0.679 (+/-0.293) for {'C': 144543977.0745926, 'kernel': 'rbf', 'gamma': 3.287002239687748e-07}
0.679 (+/-0.293) for {'C': 144543977.0745926, 'kernel': 'rbf', 'gamma': 3.299134337888643e-07}
0.678 (+/-0.293) for {'C': 144543977.0745926, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.678 (+/-0.293) for {'C': 144543977.0745926, 'kernel': 'rbf', 'gamma': 3.3235330357747726e-07}
0.678 (+/-0.291) for {'C': 144543977.0745926, 'kernel': 'rbf', 'gamma': 3.3357999666204803e-07}
0.661 (+/-0.311) for {'C': 144543977.0745926, 'kernel': 'rbf', 'gamma': 3.3481121738605415e-07}
0.679 (+/-0.289) for {'C': 144543977.0745926, 'kernel': 'rbf', 'gamma': 3.3604698246069916e-07}
0.678 (+/-0.285) for {'C': 144543977.0745926, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.677 (+/-0.292) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.678 (+/-0.292) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.2628717214308483e-07}
0.678 (+/-0.292) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.2749147555557826e-07}
0.679 (+/-0.293) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.287002239687748e-07}
0.679 (+/-0.293) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.299134337888643e-07}
0.695 (+/-0.304) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.679 (+/-0.292) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.3235330357747726e-07}
0.678 (+/-0.291) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.3357999666204803e-07}
0.661 (+/-0.311) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.3481121738605415e-07}
0.679 (+/-0.289) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.3604698246069916e-07}
0.678 (+/-0.285) for {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.677 (+/-0.292) for {'C': 173780082.87493756, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.678 (+/-0.292) for {'C': 173780082.87493756, 'kernel': 'rbf', 'gamma': 3.2628717214308483e-07}
0.678 (+/-0.292) for {'C': 173780082.87493756, 'kernel': 'rbf', 'gamma': 3.2749147555557826e-07}
0.679 (+/-0.293) for {'C': 173780082.87493756, 'kernel': 'rbf', 'gamma': 3.287002239687748e-07}
0.679 (+/-0.293) for {'C': 173780082.87493756, 'kernel': 'rbf', 'gamma': 3.299134337888643e-07}
0.678 (+/-0.292) for {'C': 173780082.87493756, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.678 (+/-0.293) for {'C': 173780082.87493756, 'kernel': 'rbf', 'gamma': 3.3235330357747726e-07}
0.678 (+/-0.291) for {'C': 173780082.87493756, 'kernel': 'rbf', 'gamma': 3.3357999666204803e-07}
0.661 (+/-0.311) for {'C': 173780082.87493756, 'kernel': 'rbf', 'gamma': 3.3481121738605415e-07}
0.679 (+/-0.289) for {'C': 173780082.87493756, 'kernel': 'rbf', 'gamma': 3.3604698246069916e-07}
0.678 (+/-0.285) for {'C': 173780082.87493756, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.677 (+/-0.292) for {'C': 190546071.79632485, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.677 (+/-0.292) for {'C': 190546071.79632485, 'kernel': 'rbf', 'gamma': 3.2628717214308483e-07}
0.678 (+/-0.292) for {'C': 190546071.79632485, 'kernel': 'rbf', 'gamma': 3.2749147555557826e-07}
0.679 (+/-0.293) for {'C': 190546071.79632485, 'kernel': 'rbf', 'gamma': 3.287002239687748e-07}
0.679 (+/-0.293) for {'C': 190546071.79632485, 'kernel': 'rbf', 'gamma': 3.299134337888643e-07}
0.678 (+/-0.292) for {'C': 190546071.79632485, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.678 (+/-0.293) for {'C': 190546071.79632485, 'kernel': 'rbf', 'gamma': 3.3235330357747726e-07}
0.678 (+/-0.291) for {'C': 190546071.79632485, 'kernel': 'rbf', 'gamma': 3.3357999666204803e-07}
0.678 (+/-0.290) for {'C': 190546071.79632485, 'kernel': 'rbf', 'gamma': 3.3481121738605415e-07}
0.679 (+/-0.289) for {'C': 190546071.79632485, 'kernel': 'rbf', 'gamma': 3.3604698246069916e-07}
0.678 (+/-0.285) for {'C': 190546071.79632485, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.677 (+/-0.291) for {'C': 208929613.08540368, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.677 (+/-0.292) for {'C': 208929613.08540368, 'kernel': 'rbf', 'gamma': 3.2628717214308483e-07}
0.678 (+/-0.292) for {'C': 208929613.08540368, 'kernel': 'rbf', 'gamma': 3.2749147555557826e-07}
0.679 (+/-0.293) for {'C': 208929613.08540368, 'kernel': 'rbf', 'gamma': 3.287002239687748e-07}
0.679 (+/-0.293) for {'C': 208929613.08540368, 'kernel': 'rbf', 'gamma': 3.299134337888643e-07}
0.678 (+/-0.292) for {'C': 208929613.08540368, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.678 (+/-0.293) for {'C': 208929613.08540368, 'kernel': 'rbf', 'gamma': 3.3235330357747726e-07}
0.678 (+/-0.291) for {'C': 208929613.08540368, 'kernel': 'rbf', 'gamma': 3.3357999666204803e-07}
0.678 (+/-0.290) for {'C': 208929613.08540368, 'kernel': 'rbf', 'gamma': 3.3481121738605415e-07}
0.679 (+/-0.289) for {'C': 208929613.08540368, 'kernel': 'rbf', 'gamma': 3.3604698246069916e-07}
0.678 (+/-0.285) for {'C': 208929613.08540368, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.677 (+/-0.291) for {'C': 229086765.27677715, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.677 (+/-0.292) for {'C': 229086765.27677715, 'kernel': 'rbf', 'gamma': 3.2628717214308483e-07}
0.678 (+/-0.292) for {'C': 229086765.27677715, 'kernel': 'rbf', 'gamma': 3.2749147555557826e-07}
0.679 (+/-0.293) for {'C': 229086765.27677715, 'kernel': 'rbf', 'gamma': 3.287002239687748e-07}
0.679 (+/-0.293) for {'C': 229086765.27677715, 'kernel': 'rbf', 'gamma': 3.299134337888643e-07}
0.678 (+/-0.292) for {'C': 229086765.27677715, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.678 (+/-0.293) for {'C': 229086765.27677715, 'kernel': 'rbf', 'gamma': 3.3235330357747726e-07}
0.678 (+/-0.291) for {'C': 229086765.27677715, 'kernel': 'rbf', 'gamma': 3.3357999666204803e-07}
0.678 (+/-0.290) for {'C': 229086765.27677715, 'kernel': 'rbf', 'gamma': 3.3481121738605415e-07}
0.679 (+/-0.289) for {'C': 229086765.27677715, 'kernel': 'rbf', 'gamma': 3.3604698246069916e-07}
0.678 (+/-0.285) for {'C': 229086765.27677715, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.677 (+/-0.291) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.2508729738543387e-07}
0.677 (+/-0.292) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.2628717214308483e-07}
0.678 (+/-0.292) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.2749147555557826e-07}
0.679 (+/-0.293) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.287002239687748e-07}
0.679 (+/-0.293) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.299134337888643e-07}
0.695 (+/-0.304) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
0.679 (+/-0.292) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.3235330357747726e-07}
0.678 (+/-0.291) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.3357999666204803e-07}
0.678 (+/-0.290) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.3481121738605415e-07}
0.679 (+/-0.289) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.3604698246069916e-07}
0.678 (+/-0.285) for {'C': 251188643.15095797, 'kernel': 'rbf', 'gamma': 3.372873086588689e-07}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.666 (+/-0.316) for {'C': 1.0, 'kernel': 'linear'}
0.662 (+/-0.314) for {'C': 10.0, 'kernel': 'linear'}
0.660 (+/-0.315) for {'C': 100.0, 'kernel': 'linear'}
0.637 (+/-0.238) for {'C': 1000.0, 'kernel': 'linear'}
0.636 (+/-0.240) for {'C': 10000.0, 'kernel': 'linear'}
0.610 (+/-0.245) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6948687031082529
第2轮loop中,tunemodel函数得到的最优参数是: ([{'C': array([1.44543977e+08, 1.47231250e+08, 1.49968484e+08, 1.52756606e+08,
1.55596563e+08, 1.58489319e+08, 1.61435856e+08, 1.64437172e+08,
1.67494288e+08, 1.70608239e+08, 1.73780083e+08]), 'kernel': ['rbf'], 'gamma': array([3.29913434e-07, 3.30156613e-07, 3.30399971e-07, 3.30643508e-07,
3.30887225e-07, 3.31131121e-07, 3.31375198e-07, 3.31619454e-07,
3.31863890e-07, 3.32108507e-07, 3.32353304e-07])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}], {'C': 158489319.24611127, 'kernel': 'rbf', 'gamma': 3.3113112148259127e-07}, 0.6948687031082529)
这是第2次迭代微调C和gamma。
第2次迭代,得到delta: [0. 0.]
SVC模型clf_op_iter的参数是: {'kernel': 'rbf', 'decision_function_shape': 'ovr', 'class_weight': {-1: 15}, 'tol': 0.001, 'gamma': 3.3113112148259127e-07, 'random_state': 0, 'cache_size': 200, 'verbose': False, 'degree': 3, 'C': 158489319.24611127, 'probability': False, 'shrinking': True, 'coef0': 0.0, 'max_iter': -1}
训练模型SVC对训练样本的预测准确率: 0.9355067328136074
测试集中,预测为舞弊样本的有: (array([ 56, 136, 370, 590, 769, 1017, 1122, 1246, 1247, 1248, 1251,
1252, 1255, 1256], dtype=int64),)
测试集中,实际为舞弊样本的有: (array([1246, 1247, 1248, 1249, 1250, 1251, 1252, 1253, 1254, 1255, 1256],
dtype=int64),)
预测的舞弊样本数目: 14
训练模型SVC对测试样本的预测准确率: 0.9525159461374911
以上是第32次特征筛选。
第32次特征筛选,AUC值是: 0.8153728294177732
X_train_iter_svc.shape is: (1257, 20)
X_test_iter_svc.shape is: (1257, 20)
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.649 (+/-0.386) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.747 (+/-0.449) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.614 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.641 (+/-0.236) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6413461538461539
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.764 (+/-0.421) for {'C': 1.0, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.620 (+/-0.321) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.20 0.17 0.18 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.683 (+/-0.296) for {'C': 1.0, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.612 (+/-0.241) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.20 0.17 0.18 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.99 0.99 629
本轮grid search结果,得到最好的参数选择是: {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6826923076923077
RBF的粗grid search最佳超参: {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001} RBF的粗grid search最高分值: 0.6413461538461539
linear的粗grid search最佳超参: {'C': 1.0, 'kernel': 'linear'} linear的粗grid search最高分值: 0.6826923076923077
粗grid search得到的parameter是:
[{'C': array([1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02, 1.e+03, 1.e+04, 1.e+05,
1.e+06, 1.e+07, 1.e+08]), 'kernel': ['rbf'], 'gamma': array([1.e-08, 1.e-07, 1.e-06, 1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01,
1.e+00, 1.e+01, 1.e+02])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.722 (+/-0.473) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.631 (+/-0.319) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.437) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.639 (+/-0.326) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.647 (+/-0.387) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.449) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.664 (+/-0.317) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.649 (+/-0.386) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.798 (+/-0.437) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.664 (+/-0.317) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.626 (+/-0.318) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.622 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.649 (+/-0.386) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.747 (+/-0.502) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.798 (+/-0.437) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.664 (+/-0.317) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.626 (+/-0.318) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.622 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.649 (+/-0.386) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.797 (+/-0.492) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.823 (+/-0.451) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.651 (+/-0.308) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.631 (+/-0.319) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.619 (+/-0.321) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.622 (+/-0.319) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.649 (+/-0.386) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.699 (+/-0.661) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.768 (+/-0.475) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.731 (+/-0.401) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.642 (+/-0.316) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.620 (+/-0.321) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.620 (+/-0.320) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.622 (+/-0.319) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.649 (+/-0.386) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.700 (+/-0.660) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.607 (+/-0.395) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.706 (+/-0.383) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.641 (+/-0.318) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.614 (+/-0.327) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.614 (+/-0.327) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.622 (+/-0.319) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.649 (+/-0.386) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'linear'}
0.673 (+/-0.330) for {'C': 10.0, 'kernel': 'linear'}
0.646 (+/-0.317) for {'C': 100.0, 'kernel': 'linear'}
0.620 (+/-0.321) for {'C': 1000.0, 'kernel': 'linear'}
0.618 (+/-0.322) for {'C': 10000.0, 'kernel': 'linear'}
0.624 (+/-0.319) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.006) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.240) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.006) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.641 (+/-0.236) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.665 (+/-0.314) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.587 (+/-0.233) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.006) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.658 (+/-0.216) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.665 (+/-0.315) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.612 (+/-0.242) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.613 (+/-0.240) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.006) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.617 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.658 (+/-0.216) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.665 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.612 (+/-0.242) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.587 (+/-0.233) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.613 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.006) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.642 (+/-0.236) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.683 (+/-0.296) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.665 (+/-0.314) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.612 (+/-0.243) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.612 (+/-0.239) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.587 (+/-0.234) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.613 (+/-0.238) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.006) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.633 (+/-0.226) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.666 (+/-0.314) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.699 (+/-0.308) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.663 (+/-0.313) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.610 (+/-0.244) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.612 (+/-0.240) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.587 (+/-0.235) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.613 (+/-0.238) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.006) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.633 (+/-0.225) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.590 (+/-0.314) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.658 (+/-0.311) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.662 (+/-0.314) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.585 (+/-0.237) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.587 (+/-0.234) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.587 (+/-0.235) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.613 (+/-0.238) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.006) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'linear'}
0.665 (+/-0.315) for {'C': 10.0, 'kernel': 'linear'}
0.664 (+/-0.316) for {'C': 100.0, 'kernel': 'linear'}
0.612 (+/-0.241) for {'C': 1000.0, 'kernel': 'linear'}
0.611 (+/-0.239) for {'C': 10000.0, 'kernel': 'linear'}
0.611 (+/-0.245) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.14 0.17 0.15 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6988782051282051
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.437) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.388) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.674 (+/-0.358) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.618 (+/-0.322) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.687 (+/-0.359) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.647 (+/-0.387) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.687 (+/-0.359) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.634 (+/-0.310) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.649 (+/-0.386) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.687 (+/-0.359) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.634 (+/-0.310) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.619 (+/-0.321) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.622 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.649 (+/-0.386) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.747 (+/-0.502) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.797 (+/-0.492) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.674 (+/-0.354) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.634 (+/-0.310) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.620 (+/-0.321) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.622 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.649 (+/-0.386) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.547 (+/-0.301) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.798 (+/-0.492) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.703 (+/-0.357) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.630 (+/-0.311) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.619 (+/-0.321) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.619 (+/-0.321) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.622 (+/-0.319) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.649 (+/-0.386) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.699 (+/-0.797) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.621 (+/-0.391) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.677 (+/-0.350) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.633 (+/-0.323) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.620 (+/-0.321) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.620 (+/-0.320) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.622 (+/-0.319) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.649 (+/-0.386) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.550 (+/-0.827) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.601 (+/-0.398) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.680 (+/-0.373) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.640 (+/-0.314) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.614 (+/-0.327) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.614 (+/-0.327) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.622 (+/-0.319) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.649 (+/-0.386) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.764 (+/-0.421) for {'C': 1.0, 'kernel': 'linear'}
0.643 (+/-0.306) for {'C': 10.0, 'kernel': 'linear'}
0.630 (+/-0.312) for {'C': 100.0, 'kernel': 'linear'}
0.620 (+/-0.321) for {'C': 1000.0, 'kernel': 'linear'}
0.618 (+/-0.322) for {'C': 10000.0, 'kernel': 'linear'}
0.624 (+/-0.319) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.497 (+/-0.007) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.681 (+/-0.294) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.612 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.006) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.306) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.587 (+/-0.234) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.240) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.006) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.698 (+/-0.306) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.662 (+/-0.314) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.587 (+/-0.234) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.006) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.698 (+/-0.306) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.662 (+/-0.315) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.611 (+/-0.242) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.613 (+/-0.240) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.006) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.617 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.642 (+/-0.236) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.697 (+/-0.305) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.662 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.611 (+/-0.243) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.587 (+/-0.233) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.613 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.006) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.525 (+/-0.150) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.667 (+/-0.316) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.698 (+/-0.306) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.661 (+/-0.314) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.610 (+/-0.242) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.612 (+/-0.239) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.587 (+/-0.234) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.613 (+/-0.238) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.006) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.642 (+/-0.236) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.612 (+/-0.319) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.697 (+/-0.307) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.658 (+/-0.313) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.610 (+/-0.244) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.612 (+/-0.240) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.587 (+/-0.235) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.613 (+/-0.238) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.006) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.592 (+/-0.228) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.583 (+/-0.305) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.656 (+/-0.310) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.683 (+/-0.294) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.585 (+/-0.237) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.587 (+/-0.234) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.587 (+/-0.235) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.613 (+/-0.238) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.006) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.683 (+/-0.296) for {'C': 1.0, 'kernel': 'linear'}
0.664 (+/-0.314) for {'C': 10.0, 'kernel': 'linear'}
0.660 (+/-0.316) for {'C': 100.0, 'kernel': 'linear'}
0.612 (+/-0.241) for {'C': 1000.0, 'kernel': 'linear'}
0.611 (+/-0.239) for {'C': 10000.0, 'kernel': 'linear'}
0.611 (+/-0.245) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.12 0.17 0.14 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6982361488993323
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.797 (+/-0.492) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.797 (+/-0.492) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.798 (+/-0.492) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.747 (+/-0.502) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.798 (+/-0.492) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.823 (+/-0.451) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.748 (+/-0.449) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.752 (+/-0.430) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.698 (+/-0.383) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.681 (+/-0.373) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.651 (+/-0.308) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.798 (+/-0.492) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.797 (+/-0.492) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.798 (+/-0.492) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.848 (+/-0.459) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.739 (+/-0.452) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.798 (+/-0.437) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.714 (+/-0.418) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.726 (+/-0.394) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.673 (+/-0.330) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.664 (+/-0.326) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.648 (+/-0.306) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.798 (+/-0.492) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.797 (+/-0.492) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.798 (+/-0.492) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.848 (+/-0.460) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.706 (+/-0.418) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.773 (+/-0.417) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.683 (+/-0.355) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.723 (+/-0.358) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.664 (+/-0.326) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.652 (+/-0.313) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.641 (+/-0.306) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.798 (+/-0.492) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.798 (+/-0.492) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.798 (+/-0.492) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.748 (+/-0.389) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.681 (+/-0.380) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.691 (+/-0.357) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.666 (+/-0.371) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.704 (+/-0.353) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.681 (+/-0.373) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.652 (+/-0.313) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.647 (+/-0.314) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.773 (+/-0.473) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.797 (+/-0.492) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.798 (+/-0.492) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.731 (+/-0.394) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.662 (+/-0.373) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.715 (+/-0.368) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.675 (+/-0.375) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.668 (+/-0.370) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.647 (+/-0.341) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.660 (+/-0.327) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.642 (+/-0.316) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.768 (+/-0.475) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.797 (+/-0.492) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.798 (+/-0.492) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.702 (+/-0.355) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.675 (+/-0.431) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.731 (+/-0.401) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.641 (+/-0.307) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.677 (+/-0.374) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.660 (+/-0.327) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.662 (+/-0.298) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.642 (+/-0.316) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.717 (+/-0.473) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.798 (+/-0.492) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.773 (+/-0.474) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.702 (+/-0.355) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.667 (+/-0.379) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.731 (+/-0.400) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.641 (+/-0.307) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.666 (+/-0.371) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.656 (+/-0.328) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.655 (+/-0.296) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.641 (+/-0.318) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.717 (+/-0.473) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.798 (+/-0.492) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.773 (+/-0.474) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.702 (+/-0.355) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.671 (+/-0.377) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.689 (+/-0.382) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.633 (+/-0.300) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.666 (+/-0.371) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.656 (+/-0.328) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.655 (+/-0.296) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.641 (+/-0.318) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.614 (+/-0.397) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.798 (+/-0.492) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.764 (+/-0.478) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.702 (+/-0.355) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.673 (+/-0.377) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.714 (+/-0.360) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.634 (+/-0.308) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.633 (+/-0.298) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.673 (+/-0.376) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.655 (+/-0.296) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.641 (+/-0.318) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.662 (+/-0.449) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.798 (+/-0.492) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.739 (+/-0.452) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.689 (+/-0.352) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.664 (+/-0.382) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.706 (+/-0.383) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.628 (+/-0.299) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.633 (+/-0.298) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.656 (+/-0.328) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.655 (+/-0.296) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.641 (+/-0.318) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.607 (+/-0.395) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.798 (+/-0.492) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.764 (+/-0.478) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.689 (+/-0.352) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.673 (+/-0.375) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.706 (+/-0.383) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.665 (+/-0.373) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.633 (+/-0.298) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.653 (+/-0.329) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.653 (+/-0.297) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.641 (+/-0.318) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'linear'}
0.673 (+/-0.330) for {'C': 10.0, 'kernel': 'linear'}
0.646 (+/-0.317) for {'C': 100.0, 'kernel': 'linear'}
0.620 (+/-0.321) for {'C': 1000.0, 'kernel': 'linear'}
0.618 (+/-0.322) for {'C': 10000.0, 'kernel': 'linear'}
0.624 (+/-0.319) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.642 (+/-0.236) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.642 (+/-0.236) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.667 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.617 (+/-0.238) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.667 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.683 (+/-0.296) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.666 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.699 (+/-0.309) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.666 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.665 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.665 (+/-0.314) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.667 (+/-0.316) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.642 (+/-0.236) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.667 (+/-0.316) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.658 (+/-0.217) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.666 (+/-0.316) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.700 (+/-0.309) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.666 (+/-0.316) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.699 (+/-0.307) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.665 (+/-0.316) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.665 (+/-0.316) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.664 (+/-0.314) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.667 (+/-0.316) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.642 (+/-0.236) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.667 (+/-0.316) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.683 (+/-0.296) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.666 (+/-0.315) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.700 (+/-0.309) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.681 (+/-0.294) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.699 (+/-0.306) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.665 (+/-0.316) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.664 (+/-0.315) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.664 (+/-0.313) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.667 (+/-0.316) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.667 (+/-0.316) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.667 (+/-0.316) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.683 (+/-0.296) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.665 (+/-0.316) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.698 (+/-0.307) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.665 (+/-0.313) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.698 (+/-0.305) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.665 (+/-0.317) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.664 (+/-0.315) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.664 (+/-0.314) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.666 (+/-0.315) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.642 (+/-0.236) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.667 (+/-0.316) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.682 (+/-0.296) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.664 (+/-0.315) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.699 (+/-0.308) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.664 (+/-0.312) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.656 (+/-0.307) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.640 (+/-0.326) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.664 (+/-0.317) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.663 (+/-0.313) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.666 (+/-0.314) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.642 (+/-0.236) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.667 (+/-0.316) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.682 (+/-0.294) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.664 (+/-0.316) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.699 (+/-0.308) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.664 (+/-0.311) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.665 (+/-0.314) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.664 (+/-0.316) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.688 (+/-0.299) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.663 (+/-0.313) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.641 (+/-0.323) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.667 (+/-0.316) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.667 (+/-0.316) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.682 (+/-0.294) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.680 (+/-0.294) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.682 (+/-0.295) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.664 (+/-0.312) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.664 (+/-0.313) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.664 (+/-0.316) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.688 (+/-0.298) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.663 (+/-0.313) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.641 (+/-0.323) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.667 (+/-0.316) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.667 (+/-0.316) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.682 (+/-0.294) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.696 (+/-0.306) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.666 (+/-0.315) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.663 (+/-0.311) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.664 (+/-0.313) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.664 (+/-0.317) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.688 (+/-0.298) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.663 (+/-0.313) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.591 (+/-0.317) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.667 (+/-0.316) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.666 (+/-0.316) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.682 (+/-0.294) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.697 (+/-0.306) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.682 (+/-0.295) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.663 (+/-0.311) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.664 (+/-0.313) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.664 (+/-0.317) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.688 (+/-0.298) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.662 (+/-0.313) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.616 (+/-0.323) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.667 (+/-0.316) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.666 (+/-0.316) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.682 (+/-0.294) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.679 (+/-0.294) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.658 (+/-0.311) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.663 (+/-0.311) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.664 (+/-0.313) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.664 (+/-0.317) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.688 (+/-0.299) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.662 (+/-0.314) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.590 (+/-0.314) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.667 (+/-0.316) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.666 (+/-0.316) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.682 (+/-0.294) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.697 (+/-0.306) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.658 (+/-0.311) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.664 (+/-0.311) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.664 (+/-0.313) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.664 (+/-0.316) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.688 (+/-0.299) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.662 (+/-0.314) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'linear'}
0.665 (+/-0.315) for {'C': 10.0, 'kernel': 'linear'}
0.664 (+/-0.316) for {'C': 100.0, 'kernel': 'linear'}
0.612 (+/-0.241) for {'C': 1000.0, 'kernel': 'linear'}
0.611 (+/-0.239) for {'C': 10000.0, 'kernel': 'linear'}
0.611 (+/-0.245) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6996794871794872
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.798 (+/-0.492) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.798 (+/-0.492) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.798 (+/-0.492) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.814 (+/-0.459) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.673 (+/-0.370) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.703 (+/-0.357) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.657 (+/-0.358) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.687 (+/-0.358) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.647 (+/-0.308) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.629 (+/-0.300) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.630 (+/-0.311) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.798 (+/-0.492) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.798 (+/-0.492) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.798 (+/-0.492) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.706 (+/-0.352) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.643 (+/-0.372) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.651 (+/-0.293) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.642 (+/-0.287) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.683 (+/-0.354) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.637 (+/-0.308) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.632 (+/-0.310) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.627 (+/-0.313) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.714 (+/-0.418) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.739 (+/-0.452) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.714 (+/-0.418) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.677 (+/-0.350) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.638 (+/-0.375) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.656 (+/-0.288) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.640 (+/-0.289) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.679 (+/-0.315) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.637 (+/-0.308) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.631 (+/-0.312) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.625 (+/-0.315) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.681 (+/-0.372) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.714 (+/-0.418) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.706 (+/-0.418) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.669 (+/-0.353) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.616 (+/-0.304) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.653 (+/-0.287) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.622 (+/-0.299) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.688 (+/-0.357) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.634 (+/-0.309) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.628 (+/-0.313) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.633 (+/-0.323) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.624 (+/-0.389) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.706 (+/-0.418) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.731 (+/-0.394) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.666 (+/-0.354) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.611 (+/-0.279) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.664 (+/-0.300) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.634 (+/-0.308) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.680 (+/-0.355) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.631 (+/-0.311) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.628 (+/-0.314) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.639 (+/-0.315) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.621 (+/-0.391) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.689 (+/-0.382) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.719 (+/-0.399) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.657 (+/-0.358) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.600 (+/-0.287) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.677 (+/-0.350) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.626 (+/-0.300) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.654 (+/-0.375) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.630 (+/-0.311) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.628 (+/-0.314) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.633 (+/-0.323) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.620 (+/-0.391) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.673 (+/-0.370) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.706 (+/-0.404) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.643 (+/-0.365) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.608 (+/-0.279) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.677 (+/-0.349) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.626 (+/-0.300) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.653 (+/-0.376) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.625 (+/-0.311) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.628 (+/-0.314) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.639 (+/-0.315) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.612 (+/-0.391) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.673 (+/-0.370) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.680 (+/-0.358) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.643 (+/-0.365) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.614 (+/-0.279) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.635 (+/-0.299) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.617 (+/-0.291) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.653 (+/-0.376) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.624 (+/-0.312) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.628 (+/-0.314) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.639 (+/-0.315) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.601 (+/-0.397) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.668 (+/-0.367) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.676 (+/-0.360) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.613 (+/-0.292) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.611 (+/-0.280) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.660 (+/-0.290) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.618 (+/-0.300) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.625 (+/-0.300) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.624 (+/-0.312) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.628 (+/-0.314) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.640 (+/-0.314) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.601 (+/-0.398) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.660 (+/-0.367) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.675 (+/-0.361) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.638 (+/-0.367) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.600 (+/-0.278) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.683 (+/-0.378) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.613 (+/-0.289) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.625 (+/-0.300) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.624 (+/-0.312) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.633 (+/-0.306) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.640 (+/-0.314) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.601 (+/-0.398) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.664 (+/-0.366) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.674 (+/-0.362) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.613 (+/-0.292) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.610 (+/-0.277) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.678 (+/-0.375) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.650 (+/-0.371) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.623 (+/-0.301) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.624 (+/-0.312) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.627 (+/-0.314) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.640 (+/-0.314) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.764 (+/-0.421) for {'C': 1.0, 'kernel': 'linear'}
0.643 (+/-0.306) for {'C': 10.0, 'kernel': 'linear'}
0.630 (+/-0.312) for {'C': 100.0, 'kernel': 'linear'}
0.620 (+/-0.321) for {'C': 1000.0, 'kernel': 'linear'}
0.618 (+/-0.322) for {'C': 10000.0, 'kernel': 'linear'}
0.624 (+/-0.319) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.667 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.667 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.667 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.683 (+/-0.296) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.665 (+/-0.315) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.698 (+/-0.306) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.679 (+/-0.288) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.697 (+/-0.304) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.664 (+/-0.315) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.663 (+/-0.314) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.661 (+/-0.314) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.667 (+/-0.316) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.667 (+/-0.316) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.667 (+/-0.316) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.682 (+/-0.294) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.663 (+/-0.312) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.697 (+/-0.305) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.680 (+/-0.290) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.697 (+/-0.304) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.663 (+/-0.313) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.662 (+/-0.315) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.659 (+/-0.315) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.666 (+/-0.316) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.666 (+/-0.316) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.666 (+/-0.316) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.681 (+/-0.293) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.662 (+/-0.313) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.698 (+/-0.306) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.679 (+/-0.288) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.697 (+/-0.305) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.663 (+/-0.314) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.661 (+/-0.315) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.658 (+/-0.313) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.665 (+/-0.313) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.666 (+/-0.316) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.666 (+/-0.316) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.681 (+/-0.291) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.662 (+/-0.314) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.697 (+/-0.306) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.663 (+/-0.309) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.697 (+/-0.306) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.662 (+/-0.314) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.659 (+/-0.315) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.658 (+/-0.313) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.614 (+/-0.321) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.666 (+/-0.315) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.682 (+/-0.296) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.680 (+/-0.290) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.679 (+/-0.291) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.697 (+/-0.307) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.663 (+/-0.309) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.680 (+/-0.293) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.662 (+/-0.314) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.659 (+/-0.316) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.683 (+/-0.295) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.612 (+/-0.319) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.666 (+/-0.315) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.682 (+/-0.295) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.680 (+/-0.288) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.661 (+/-0.312) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.697 (+/-0.307) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.663 (+/-0.310) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.663 (+/-0.312) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.661 (+/-0.314) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.658 (+/-0.317) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.658 (+/-0.313) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.612 (+/-0.319) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.665 (+/-0.314) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.682 (+/-0.295) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.679 (+/-0.286) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.677 (+/-0.291) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.681 (+/-0.294) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.662 (+/-0.310) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.662 (+/-0.311) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.661 (+/-0.313) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.659 (+/-0.316) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.683 (+/-0.295) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.610 (+/-0.314) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.665 (+/-0.314) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.681 (+/-0.295) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.679 (+/-0.286) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.694 (+/-0.304) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.664 (+/-0.314) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.662 (+/-0.309) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.662 (+/-0.311) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.661 (+/-0.313) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.658 (+/-0.317) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.683 (+/-0.294) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.584 (+/-0.306) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.665 (+/-0.313) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.681 (+/-0.295) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.678 (+/-0.286) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.693 (+/-0.303) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.680 (+/-0.295) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.662 (+/-0.309) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.662 (+/-0.312) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.661 (+/-0.313) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.658 (+/-0.317) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.683 (+/-0.294) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.583 (+/-0.305) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.665 (+/-0.312) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.681 (+/-0.295) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.678 (+/-0.286) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.676 (+/-0.290) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.656 (+/-0.311) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.662 (+/-0.309) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.662 (+/-0.312) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.660 (+/-0.313) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.683 (+/-0.297) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.683 (+/-0.294) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.583 (+/-0.305) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.665 (+/-0.313) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.681 (+/-0.295) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.678 (+/-0.286) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.98107170553497e-07}
0.693 (+/-0.303) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.656 (+/-0.310) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.662 (+/-0.309) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.662 (+/-0.312) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.660 (+/-0.314) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.658 (+/-0.316) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.683 (+/-0.294) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.683 (+/-0.296) for {'C': 1.0, 'kernel': 'linear'}
0.664 (+/-0.314) for {'C': 10.0, 'kernel': 'linear'}
0.660 (+/-0.316) for {'C': 100.0, 'kernel': 'linear'}
0.612 (+/-0.241) for {'C': 1000.0, 'kernel': 'linear'}
0.611 (+/-0.239) for {'C': 10000.0, 'kernel': 'linear'}
0.611 (+/-0.245) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.12 0.17 0.14 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6982361488993323
循环迭代之前,delta is: [8.41510681e+06 2.11758237e-22]
执行tunemodel()函数前,使用的grid search parameter是:
[{'C': array([1000000. , 1096478.19614319, 1202264.43461741,
1318256.73855641, 1445439.77074593, 1584893.19246111,
1737800.82874938, 1905460.71796325, 2089296.13085404,
2290867.65276777, 2511886.43150958]), 'kernel': ['rbf'], 'gamma': array([6.30957344e-07, 6.91830971e-07, 7.58577575e-07, 8.31763771e-07,
9.12010839e-07, 1.00000000e-06, 1.09647820e-06, 1.20226443e-06,
1.31825674e-06, 1.44543977e-06, 1.58489319e-06])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.798 (+/-0.492) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.798 (+/-0.492) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.773 (+/-0.474) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.773 (+/-0.474) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.773 (+/-0.474) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.823 (+/-0.451) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.798 (+/-0.437) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.748 (+/-0.449) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.798 (+/-0.437) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.747 (+/-0.449) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.748 (+/-0.449) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.798 (+/-0.492) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.798 (+/-0.492) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.773 (+/-0.474) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.748 (+/-0.449) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.773 (+/-0.474) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.798 (+/-0.437) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.798 (+/-0.437) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.748 (+/-0.449) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.798 (+/-0.437) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.748 (+/-0.449) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.748 (+/-0.449) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.773 (+/-0.474) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.773 (+/-0.474) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.773 (+/-0.474) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.748 (+/-0.449) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.739 (+/-0.452) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.798 (+/-0.437) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.798 (+/-0.437) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.723 (+/-0.417) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.798 (+/-0.437) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.748 (+/-0.449) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.748 (+/-0.449) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.748 (+/-0.449) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.748 (+/-0.449) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.748 (+/-0.449) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.748 (+/-0.449) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.714 (+/-0.418) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.798 (+/-0.437) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.798 (+/-0.437) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.723 (+/-0.417) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.773 (+/-0.416) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.748 (+/-0.449) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.748 (+/-0.449) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.748 (+/-0.449) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.748 (+/-0.449) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.748 (+/-0.449) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.748 (+/-0.449) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.714 (+/-0.418) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.798 (+/-0.437) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.798 (+/-0.437) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.723 (+/-0.417) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.773 (+/-0.416) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.748 (+/-0.449) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.723 (+/-0.417) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.739 (+/-0.452) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.748 (+/-0.449) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.714 (+/-0.418) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.723 (+/-0.417) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.714 (+/-0.418) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.798 (+/-0.437) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.773 (+/-0.416) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.723 (+/-0.417) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.748 (+/-0.389) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.739 (+/-0.452) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.714 (+/-0.418) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.739 (+/-0.452) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.748 (+/-0.449) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.714 (+/-0.418) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.714 (+/-0.418) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.714 (+/-0.418) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.798 (+/-0.437) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.739 (+/-0.392) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.689 (+/-0.375) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.739 (+/-0.392) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.714 (+/-0.418) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.706 (+/-0.418) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.739 (+/-0.452) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.748 (+/-0.449) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.714 (+/-0.418) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.723 (+/-0.417) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.714 (+/-0.418) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.773 (+/-0.417) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.740 (+/-0.392) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.714 (+/-0.352) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.731 (+/-0.394) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.714 (+/-0.418) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.691 (+/-0.422) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.739 (+/-0.452) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.748 (+/-0.449) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.714 (+/-0.418) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.723 (+/-0.417) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.714 (+/-0.418) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.773 (+/-0.417) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.740 (+/-0.392) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.714 (+/-0.352) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.740 (+/-0.392) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.698 (+/-0.423) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.674 (+/-0.383) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.714 (+/-0.418) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.739 (+/-0.452) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.714 (+/-0.418) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.714 (+/-0.418) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.760 (+/-0.426) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.773 (+/-0.417) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.740 (+/-0.392) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.706 (+/-0.353) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.740 (+/-0.392) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.689 (+/-0.422) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.674 (+/-0.383) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.706 (+/-0.418) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.739 (+/-0.452) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.714 (+/-0.418) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.714 (+/-0.418) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.760 (+/-0.426) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.773 (+/-0.417) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.731 (+/-0.394) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.706 (+/-0.353) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.701 (+/-0.351) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.668 (+/-0.371) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.683 (+/-0.355) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'linear'}
0.673 (+/-0.330) for {'C': 10.0, 'kernel': 'linear'}
0.646 (+/-0.317) for {'C': 100.0, 'kernel': 'linear'}
0.620 (+/-0.321) for {'C': 1000.0, 'kernel': 'linear'}
0.618 (+/-0.322) for {'C': 10000.0, 'kernel': 'linear'}
0.624 (+/-0.319) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.667 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.667 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.667 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.667 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.667 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.683 (+/-0.296) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.683 (+/-0.296) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.666 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.683 (+/-0.296) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.641 (+/-0.236) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.666 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.667 (+/-0.316) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.667 (+/-0.316) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.667 (+/-0.316) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.666 (+/-0.316) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.667 (+/-0.316) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.683 (+/-0.296) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.683 (+/-0.296) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.666 (+/-0.316) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.683 (+/-0.296) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.666 (+/-0.316) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.666 (+/-0.316) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.667 (+/-0.316) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.667 (+/-0.316) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.667 (+/-0.316) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.666 (+/-0.316) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.666 (+/-0.316) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.683 (+/-0.296) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.683 (+/-0.296) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.666 (+/-0.316) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.683 (+/-0.296) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.666 (+/-0.316) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.666 (+/-0.316) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.666 (+/-0.316) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.666 (+/-0.316) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.666 (+/-0.316) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.666 (+/-0.316) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.666 (+/-0.316) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.700 (+/-0.309) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.683 (+/-0.296) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.666 (+/-0.316) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.683 (+/-0.296) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.666 (+/-0.317) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.666 (+/-0.316) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.666 (+/-0.316) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.666 (+/-0.316) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.666 (+/-0.316) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.666 (+/-0.316) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.666 (+/-0.316) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.700 (+/-0.309) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.683 (+/-0.296) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.666 (+/-0.316) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.683 (+/-0.296) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.666 (+/-0.317) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.666 (+/-0.316) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.666 (+/-0.316) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.666 (+/-0.316) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.666 (+/-0.316) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.666 (+/-0.316) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.666 (+/-0.316) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.700 (+/-0.309) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.683 (+/-0.296) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.666 (+/-0.316) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.683 (+/-0.296) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.666 (+/-0.317) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.666 (+/-0.316) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.666 (+/-0.316) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.666 (+/-0.316) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.666 (+/-0.316) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.666 (+/-0.316) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.666 (+/-0.316) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.700 (+/-0.309) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.683 (+/-0.296) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.666 (+/-0.316) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.683 (+/-0.296) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.666 (+/-0.317) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.666 (+/-0.315) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.666 (+/-0.316) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.666 (+/-0.316) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.666 (+/-0.316) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.666 (+/-0.316) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.666 (+/-0.316) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.700 (+/-0.309) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.699 (+/-0.308) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.682 (+/-0.296) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.682 (+/-0.296) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.666 (+/-0.317) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.665 (+/-0.315) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.666 (+/-0.316) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.666 (+/-0.316) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.666 (+/-0.316) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.666 (+/-0.316) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.666 (+/-0.316) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.700 (+/-0.309) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.699 (+/-0.308) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.682 (+/-0.296) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.699 (+/-0.308) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.665 (+/-0.316) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.665 (+/-0.314) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.666 (+/-0.316) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.666 (+/-0.316) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.666 (+/-0.316) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.666 (+/-0.316) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.683 (+/-0.296) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.700 (+/-0.309) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.699 (+/-0.308) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.682 (+/-0.295) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.699 (+/-0.308) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.665 (+/-0.316) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.665 (+/-0.314) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.666 (+/-0.315) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.666 (+/-0.316) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.666 (+/-0.316) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.666 (+/-0.316) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.683 (+/-0.296) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.700 (+/-0.309) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.699 (+/-0.308) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.682 (+/-0.295) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.698 (+/-0.307) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.665 (+/-0.316) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.681 (+/-0.294) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'linear'}
0.665 (+/-0.315) for {'C': 10.0, 'kernel': 'linear'}
0.664 (+/-0.316) for {'C': 100.0, 'kernel': 'linear'}
0.612 (+/-0.241) for {'C': 1000.0, 'kernel': 'linear'}
0.611 (+/-0.239) for {'C': 10000.0, 'kernel': 'linear'}
0.611 (+/-0.245) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6996794871794872
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.673 (+/-0.370) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.723 (+/-0.417) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.714 (+/-0.418) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.689 (+/-0.352) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.684 (+/-0.355) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.703 (+/-0.357) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.714 (+/-0.399) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.636 (+/-0.286) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.624 (+/-0.288) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.645 (+/-0.323) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.657 (+/-0.358) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.668 (+/-0.367) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.723 (+/-0.417) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.714 (+/-0.418) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.689 (+/-0.352) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.679 (+/-0.356) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.690 (+/-0.354) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.704 (+/-0.405) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.632 (+/-0.287) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.624 (+/-0.288) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.637 (+/-0.307) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.632 (+/-0.288) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.663 (+/-0.368) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.714 (+/-0.418) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.698 (+/-0.383) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.674 (+/-0.358) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.675 (+/-0.359) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.691 (+/-0.357) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.695 (+/-0.410) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.631 (+/-0.287) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.627 (+/-0.287) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.641 (+/-0.308) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.632 (+/-0.288) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.654 (+/-0.367) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.681 (+/-0.380) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.683 (+/-0.386) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.655 (+/-0.358) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.664 (+/-0.357) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.656 (+/-0.292) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.674 (+/-0.359) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.629 (+/-0.288) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.634 (+/-0.287) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.632 (+/-0.301) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.637 (+/-0.288) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.644 (+/-0.371) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.674 (+/-0.383) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.653 (+/-0.376) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.644 (+/-0.363) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.665 (+/-0.356) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.651 (+/-0.293) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.674 (+/-0.359) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.629 (+/-0.288) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.634 (+/-0.286) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.632 (+/-0.301) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.641 (+/-0.288) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.643 (+/-0.372) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.659 (+/-0.375) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.626 (+/-0.310) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.608 (+/-0.298) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.662 (+/-0.355) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.651 (+/-0.293) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.674 (+/-0.359) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.631 (+/-0.288) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.634 (+/-0.286) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.636 (+/-0.302) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.642 (+/-0.287) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.643 (+/-0.372) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.654 (+/-0.371) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.621 (+/-0.303) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.608 (+/-0.298) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.660 (+/-0.357) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.646 (+/-0.287) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.676 (+/-0.358) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.631 (+/-0.288) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.634 (+/-0.286) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.626 (+/-0.292) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.641 (+/-0.288) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.641 (+/-0.373) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.654 (+/-0.371) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.615 (+/-0.301) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.609 (+/-0.298) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.662 (+/-0.356) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.658 (+/-0.292) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.679 (+/-0.357) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.632 (+/-0.278) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.622 (+/-0.276) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.631 (+/-0.299) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.624 (+/-0.300) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.639 (+/-0.374) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.624 (+/-0.299) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.612 (+/-0.298) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.609 (+/-0.298) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.660 (+/-0.359) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.651 (+/-0.293) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.681 (+/-0.355) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.614 (+/-0.289) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.629 (+/-0.275) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.631 (+/-0.299) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.624 (+/-0.300) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.638 (+/-0.375) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.620 (+/-0.299) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.612 (+/-0.298) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.610 (+/-0.298) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.661 (+/-0.358) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.653 (+/-0.287) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.681 (+/-0.355) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.639 (+/-0.287) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.636 (+/-0.278) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.631 (+/-0.299) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.624 (+/-0.301) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.638 (+/-0.375) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.620 (+/-0.299) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.627 (+/-0.290) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.610 (+/-0.298) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.665 (+/-0.355) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.656 (+/-0.288) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.685 (+/-0.355) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.643 (+/-0.287) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.636 (+/-0.278) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.631 (+/-0.299) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.640 (+/-0.289) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.764 (+/-0.421) for {'C': 1.0, 'kernel': 'linear'}
0.643 (+/-0.306) for {'C': 10.0, 'kernel': 'linear'}
0.630 (+/-0.312) for {'C': 100.0, 'kernel': 'linear'}
0.620 (+/-0.321) for {'C': 1000.0, 'kernel': 'linear'}
0.618 (+/-0.322) for {'C': 10000.0, 'kernel': 'linear'}
0.624 (+/-0.319) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.20 0.17 0.18 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.665 (+/-0.315) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.666 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.666 (+/-0.317) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.682 (+/-0.294) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.698 (+/-0.304) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.698 (+/-0.306) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.698 (+/-0.308) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.680 (+/-0.291) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.679 (+/-0.291) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.664 (+/-0.315) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.679 (+/-0.288) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.665 (+/-0.314) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.666 (+/-0.316) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.666 (+/-0.317) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.682 (+/-0.294) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.697 (+/-0.304) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.698 (+/-0.306) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.698 (+/-0.307) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.680 (+/-0.291) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.679 (+/-0.290) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.664 (+/-0.315) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.679 (+/-0.288) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.665 (+/-0.314) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.666 (+/-0.316) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.665 (+/-0.316) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.681 (+/-0.293) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.697 (+/-0.302) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.698 (+/-0.306) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.697 (+/-0.307) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.680 (+/-0.290) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.679 (+/-0.290) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.664 (+/-0.315) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.679 (+/-0.288) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.664 (+/-0.313) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.665 (+/-0.316) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.665 (+/-0.315) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.680 (+/-0.292) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.696 (+/-0.302) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.697 (+/-0.306) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.697 (+/-0.307) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.679 (+/-0.290) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.680 (+/-0.290) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.664 (+/-0.314) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.680 (+/-0.288) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.664 (+/-0.313) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.665 (+/-0.315) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.664 (+/-0.314) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.679 (+/-0.290) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.697 (+/-0.302) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.697 (+/-0.305) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.697 (+/-0.307) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.679 (+/-0.289) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.680 (+/-0.291) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.664 (+/-0.314) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.680 (+/-0.289) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.663 (+/-0.312) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.664 (+/-0.315) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.663 (+/-0.314) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.662 (+/-0.310) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.697 (+/-0.302) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.697 (+/-0.305) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.697 (+/-0.307) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.679 (+/-0.289) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.680 (+/-0.291) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.664 (+/-0.314) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.680 (+/-0.290) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.663 (+/-0.313) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.664 (+/-0.314) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.663 (+/-0.314) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.662 (+/-0.310) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.696 (+/-0.302) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.697 (+/-0.305) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.698 (+/-0.307) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.679 (+/-0.289) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.680 (+/-0.292) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.664 (+/-0.314) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.680 (+/-0.289) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.663 (+/-0.313) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.664 (+/-0.314) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.662 (+/-0.313) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.662 (+/-0.309) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.696 (+/-0.303) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.698 (+/-0.306) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.698 (+/-0.307) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.680 (+/-0.289) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.679 (+/-0.291) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.664 (+/-0.315) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.663 (+/-0.309) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.663 (+/-0.313) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.664 (+/-0.312) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.662 (+/-0.312) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.662 (+/-0.308) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.680 (+/-0.291) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.697 (+/-0.305) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.698 (+/-0.307) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.663 (+/-0.308) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.680 (+/-0.292) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.664 (+/-0.315) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.663 (+/-0.309) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.662 (+/-0.313) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.663 (+/-0.311) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.662 (+/-0.312) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.662 (+/-0.308) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.680 (+/-0.291) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.697 (+/-0.305) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.698 (+/-0.307) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.680 (+/-0.288) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.697 (+/-0.305) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.664 (+/-0.315) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.663 (+/-0.308) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.662 (+/-0.313) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.663 (+/-0.311) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.679 (+/-0.291) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.662 (+/-0.308) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 8.317637711026701e-07}
0.697 (+/-0.303) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.698 (+/-0.306) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.698 (+/-0.307) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.680 (+/-0.288) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.2022644346174144e-06}
0.697 (+/-0.305) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.3182567385564061e-06}
0.664 (+/-0.315) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.4454397707459273e-06}
0.679 (+/-0.288) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.683 (+/-0.296) for {'C': 1.0, 'kernel': 'linear'}
0.664 (+/-0.314) for {'C': 10.0, 'kernel': 'linear'}
0.660 (+/-0.316) for {'C': 100.0, 'kernel': 'linear'}
0.612 (+/-0.241) for {'C': 1000.0, 'kernel': 'linear'}
0.611 (+/-0.239) for {'C': 10000.0, 'kernel': 'linear'}
0.611 (+/-0.245) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.14 0.17 0.15 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6983958900156649
第0轮loop中,tunemodel函数得到的最优参数是: ([{'C': array([1202264.43461741, 1224616.19926505, 1247383.51424294,
1270574.10520854, 1294195.84144999, 1318256.73855641,
1342764.96113787, 1367728.82559585, 1393156.8029453 ,
1419057.52168909, 1445439.77074593]), 'kernel': ['rbf'], 'gamma': array([9.12010839e-07, 9.28966387e-07, 9.46237161e-07, 9.63829024e-07,
9.81747943e-07, 1.00000000e-06, 1.01859139e-06, 1.03752842e-06,
1.05681751e-06, 1.07646521e-06, 1.09647820e-06])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}], {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}, 0.6996794871794872)
这是第0次迭代微调C和gamma。
第0次迭代,得到delta: [266636.45390471 0. ]
执行tunemodel()函数前,使用的grid search parameter是:
[{'C': array([1202264.43461741, 1224616.19926505, 1247383.51424294,
1270574.10520854, 1294195.84144999, 1318256.73855641,
1342764.96113787, 1367728.82559585, 1393156.8029453 ,
1419057.52168909, 1445439.77074593]), 'kernel': ['rbf'], 'gamma': array([9.12010839e-07, 9.28966387e-07, 9.46237161e-07, 9.63829024e-07,
9.81747943e-07, 1.00000000e-06, 1.01859139e-06, 1.03752842e-06,
1.05681751e-06, 1.07646521e-06, 1.09647820e-06])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.739 (+/-0.452) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.714 (+/-0.418) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.748 (+/-0.449) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.748 (+/-0.449) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.723 (+/-0.417) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.798 (+/-0.437) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.798 (+/-0.437) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.764 (+/-0.421) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.037528415818012e-06}
0.764 (+/-0.421) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.05681750921366e-06}
0.798 (+/-0.437) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.076465213629836e-06}
0.798 (+/-0.437) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.739 (+/-0.452) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.714 (+/-0.418) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.798 (+/-0.437) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.748 (+/-0.449) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.723 (+/-0.417) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.798 (+/-0.437) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.798 (+/-0.437) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.764 (+/-0.421) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 1.037528415818012e-06}
0.764 (+/-0.421) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 1.05681750921366e-06}
0.798 (+/-0.437) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 1.076465213629836e-06}
0.798 (+/-0.437) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.739 (+/-0.452) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.714 (+/-0.418) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.798 (+/-0.437) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.748 (+/-0.449) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.748 (+/-0.449) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.798 (+/-0.437) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.798 (+/-0.437) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.764 (+/-0.421) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 1.037528415818012e-06}
0.764 (+/-0.421) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 1.05681750921366e-06}
0.798 (+/-0.437) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 1.076465213629836e-06}
0.798 (+/-0.437) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.714 (+/-0.418) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.714 (+/-0.418) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.798 (+/-0.437) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.723 (+/-0.417) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.723 (+/-0.417) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.798 (+/-0.437) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.798 (+/-0.437) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.739 (+/-0.392) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 1.037528415818012e-06}
0.764 (+/-0.421) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 1.05681750921366e-06}
0.798 (+/-0.437) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 1.076465213629836e-06}
0.798 (+/-0.437) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.714 (+/-0.418) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.714 (+/-0.418) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.798 (+/-0.437) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.723 (+/-0.417) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.723 (+/-0.417) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.798 (+/-0.437) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.798 (+/-0.437) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.739 (+/-0.392) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 1.037528415818012e-06}
0.764 (+/-0.421) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 1.05681750921366e-06}
0.798 (+/-0.437) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 1.076465213629836e-06}
0.798 (+/-0.437) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.714 (+/-0.418) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.714 (+/-0.418) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.798 (+/-0.437) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.714 (+/-0.418) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.723 (+/-0.417) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.798 (+/-0.437) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.798 (+/-0.437) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.731 (+/-0.394) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.037528415818012e-06}
0.764 (+/-0.421) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.05681750921366e-06}
0.781 (+/-0.417) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.076465213629836e-06}
0.798 (+/-0.437) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.714 (+/-0.418) for {'C': 1342764.9611378654, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.714 (+/-0.418) for {'C': 1342764.9611378654, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.773 (+/-0.416) for {'C': 1342764.9611378654, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.714 (+/-0.418) for {'C': 1342764.9611378654, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.723 (+/-0.417) for {'C': 1342764.9611378654, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.798 (+/-0.437) for {'C': 1342764.9611378654, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.798 (+/-0.437) for {'C': 1342764.9611378654, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.731 (+/-0.394) for {'C': 1342764.9611378654, 'kernel': 'rbf', 'gamma': 1.037528415818012e-06}
0.764 (+/-0.421) for {'C': 1342764.9611378654, 'kernel': 'rbf', 'gamma': 1.05681750921366e-06}
0.781 (+/-0.417) for {'C': 1342764.9611378654, 'kernel': 'rbf', 'gamma': 1.076465213629836e-06}
0.798 (+/-0.437) for {'C': 1342764.9611378654, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.714 (+/-0.418) for {'C': 1367728.8255958504, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.714 (+/-0.418) for {'C': 1367728.8255958504, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.723 (+/-0.417) for {'C': 1367728.8255958504, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.714 (+/-0.418) for {'C': 1367728.8255958504, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.723 (+/-0.417) for {'C': 1367728.8255958504, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.798 (+/-0.437) for {'C': 1367728.8255958504, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.798 (+/-0.437) for {'C': 1367728.8255958504, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.714 (+/-0.360) for {'C': 1367728.8255958504, 'kernel': 'rbf', 'gamma': 1.037528415818012e-06}
0.764 (+/-0.421) for {'C': 1367728.8255958504, 'kernel': 'rbf', 'gamma': 1.05681750921366e-06}
0.756 (+/-0.391) for {'C': 1367728.8255958504, 'kernel': 'rbf', 'gamma': 1.076465213629836e-06}
0.798 (+/-0.437) for {'C': 1367728.8255958504, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.714 (+/-0.418) for {'C': 1393156.8029453037, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.714 (+/-0.418) for {'C': 1393156.8029453037, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.723 (+/-0.417) for {'C': 1393156.8029453037, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.714 (+/-0.418) for {'C': 1393156.8029453037, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.748 (+/-0.449) for {'C': 1393156.8029453037, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.798 (+/-0.437) for {'C': 1393156.8029453037, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.798 (+/-0.437) for {'C': 1393156.8029453037, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.714 (+/-0.360) for {'C': 1393156.8029453037, 'kernel': 'rbf', 'gamma': 1.037528415818012e-06}
0.731 (+/-0.394) for {'C': 1393156.8029453037, 'kernel': 'rbf', 'gamma': 1.05681750921366e-06}
0.756 (+/-0.391) for {'C': 1393156.8029453037, 'kernel': 'rbf', 'gamma': 1.076465213629836e-06}
0.798 (+/-0.437) for {'C': 1393156.8029453037, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.714 (+/-0.418) for {'C': 1419057.5216890923, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.714 (+/-0.418) for {'C': 1419057.5216890923, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.723 (+/-0.417) for {'C': 1419057.5216890923, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.714 (+/-0.418) for {'C': 1419057.5216890923, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.748 (+/-0.449) for {'C': 1419057.5216890923, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.798 (+/-0.437) for {'C': 1419057.5216890923, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.798 (+/-0.437) for {'C': 1419057.5216890923, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.714 (+/-0.360) for {'C': 1419057.5216890923, 'kernel': 'rbf', 'gamma': 1.037528415818012e-06}
0.731 (+/-0.394) for {'C': 1419057.5216890923, 'kernel': 'rbf', 'gamma': 1.05681750921366e-06}
0.748 (+/-0.388) for {'C': 1419057.5216890923, 'kernel': 'rbf', 'gamma': 1.076465213629836e-06}
0.798 (+/-0.437) for {'C': 1419057.5216890923, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.714 (+/-0.418) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.714 (+/-0.418) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.723 (+/-0.417) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.714 (+/-0.418) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.723 (+/-0.417) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.798 (+/-0.437) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.798 (+/-0.437) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.714 (+/-0.360) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.037528415818012e-06}
0.731 (+/-0.394) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.05681750921366e-06}
0.773 (+/-0.416) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.076465213629836e-06}
0.798 (+/-0.437) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'linear'}
0.673 (+/-0.330) for {'C': 10.0, 'kernel': 'linear'}
0.646 (+/-0.317) for {'C': 100.0, 'kernel': 'linear'}
0.620 (+/-0.321) for {'C': 1000.0, 'kernel': 'linear'}
0.618 (+/-0.322) for {'C': 10000.0, 'kernel': 'linear'}
0.624 (+/-0.319) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.666 (+/-0.316) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.666 (+/-0.316) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.666 (+/-0.316) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.666 (+/-0.316) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.666 (+/-0.316) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.683 (+/-0.296) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.683 (+/-0.296) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.683 (+/-0.296) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.037528415818012e-06}
0.683 (+/-0.296) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.05681750921366e-06}
0.683 (+/-0.296) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.076465213629836e-06}
0.683 (+/-0.296) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.666 (+/-0.316) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.666 (+/-0.316) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.683 (+/-0.296) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.666 (+/-0.316) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.666 (+/-0.316) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.683 (+/-0.296) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.683 (+/-0.296) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.683 (+/-0.296) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 1.037528415818012e-06}
0.683 (+/-0.296) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 1.05681750921366e-06}
0.683 (+/-0.296) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 1.076465213629836e-06}
0.683 (+/-0.296) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.666 (+/-0.316) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.666 (+/-0.316) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.683 (+/-0.296) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.666 (+/-0.316) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.666 (+/-0.316) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.700 (+/-0.309) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.683 (+/-0.296) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.683 (+/-0.296) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 1.037528415818012e-06}
0.683 (+/-0.296) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 1.05681750921366e-06}
0.683 (+/-0.296) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 1.076465213629836e-06}
0.683 (+/-0.296) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.666 (+/-0.316) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.666 (+/-0.316) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.683 (+/-0.296) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.666 (+/-0.316) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.666 (+/-0.316) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.700 (+/-0.309) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.683 (+/-0.296) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.683 (+/-0.296) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 1.037528415818012e-06}
0.683 (+/-0.296) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 1.05681750921366e-06}
0.683 (+/-0.296) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 1.076465213629836e-06}
0.683 (+/-0.296) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.666 (+/-0.316) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.666 (+/-0.316) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.683 (+/-0.296) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.666 (+/-0.316) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.666 (+/-0.316) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.700 (+/-0.309) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.683 (+/-0.296) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.683 (+/-0.296) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 1.037528415818012e-06}
0.683 (+/-0.296) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 1.05681750921366e-06}
0.683 (+/-0.296) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 1.076465213629836e-06}
0.683 (+/-0.296) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.666 (+/-0.316) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.666 (+/-0.316) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.683 (+/-0.296) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.666 (+/-0.316) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.666 (+/-0.316) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.700 (+/-0.309) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.683 (+/-0.296) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.682 (+/-0.296) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.037528415818012e-06}
0.683 (+/-0.296) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.05681750921366e-06}
0.683 (+/-0.295) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.076465213629836e-06}
0.683 (+/-0.296) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.666 (+/-0.316) for {'C': 1342764.9611378654, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.666 (+/-0.316) for {'C': 1342764.9611378654, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.683 (+/-0.296) for {'C': 1342764.9611378654, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.666 (+/-0.316) for {'C': 1342764.9611378654, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.666 (+/-0.316) for {'C': 1342764.9611378654, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.700 (+/-0.309) for {'C': 1342764.9611378654, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.683 (+/-0.296) for {'C': 1342764.9611378654, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.682 (+/-0.296) for {'C': 1342764.9611378654, 'kernel': 'rbf', 'gamma': 1.037528415818012e-06}
0.683 (+/-0.296) for {'C': 1342764.9611378654, 'kernel': 'rbf', 'gamma': 1.05681750921366e-06}
0.683 (+/-0.295) for {'C': 1342764.9611378654, 'kernel': 'rbf', 'gamma': 1.076465213629836e-06}
0.683 (+/-0.296) for {'C': 1342764.9611378654, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.666 (+/-0.316) for {'C': 1367728.8255958504, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.666 (+/-0.316) for {'C': 1367728.8255958504, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.666 (+/-0.316) for {'C': 1367728.8255958504, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.666 (+/-0.316) for {'C': 1367728.8255958504, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.666 (+/-0.316) for {'C': 1367728.8255958504, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.700 (+/-0.309) for {'C': 1367728.8255958504, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.683 (+/-0.296) for {'C': 1367728.8255958504, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.682 (+/-0.295) for {'C': 1367728.8255958504, 'kernel': 'rbf', 'gamma': 1.037528415818012e-06}
0.683 (+/-0.296) for {'C': 1367728.8255958504, 'kernel': 'rbf', 'gamma': 1.05681750921366e-06}
0.683 (+/-0.295) for {'C': 1367728.8255958504, 'kernel': 'rbf', 'gamma': 1.076465213629836e-06}
0.683 (+/-0.296) for {'C': 1367728.8255958504, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.666 (+/-0.316) for {'C': 1393156.8029453037, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.666 (+/-0.316) for {'C': 1393156.8029453037, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.666 (+/-0.316) for {'C': 1393156.8029453037, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.666 (+/-0.316) for {'C': 1393156.8029453037, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.666 (+/-0.316) for {'C': 1393156.8029453037, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.700 (+/-0.309) for {'C': 1393156.8029453037, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.683 (+/-0.296) for {'C': 1393156.8029453037, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.682 (+/-0.295) for {'C': 1393156.8029453037, 'kernel': 'rbf', 'gamma': 1.037528415818012e-06}
0.682 (+/-0.296) for {'C': 1393156.8029453037, 'kernel': 'rbf', 'gamma': 1.05681750921366e-06}
0.683 (+/-0.295) for {'C': 1393156.8029453037, 'kernel': 'rbf', 'gamma': 1.076465213629836e-06}
0.683 (+/-0.296) for {'C': 1393156.8029453037, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.666 (+/-0.316) for {'C': 1419057.5216890923, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.666 (+/-0.316) for {'C': 1419057.5216890923, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.666 (+/-0.316) for {'C': 1419057.5216890923, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.666 (+/-0.316) for {'C': 1419057.5216890923, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.666 (+/-0.316) for {'C': 1419057.5216890923, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.700 (+/-0.309) for {'C': 1419057.5216890923, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.683 (+/-0.296) for {'C': 1419057.5216890923, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.682 (+/-0.295) for {'C': 1419057.5216890923, 'kernel': 'rbf', 'gamma': 1.037528415818012e-06}
0.682 (+/-0.296) for {'C': 1419057.5216890923, 'kernel': 'rbf', 'gamma': 1.05681750921366e-06}
0.683 (+/-0.294) for {'C': 1419057.5216890923, 'kernel': 'rbf', 'gamma': 1.076465213629836e-06}
0.683 (+/-0.296) for {'C': 1419057.5216890923, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.666 (+/-0.316) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.666 (+/-0.316) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.666 (+/-0.316) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.666 (+/-0.316) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.666 (+/-0.316) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.700 (+/-0.309) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.683 (+/-0.296) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.682 (+/-0.295) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.037528415818012e-06}
0.682 (+/-0.296) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.05681750921366e-06}
0.683 (+/-0.296) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.076465213629836e-06}
0.683 (+/-0.296) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'linear'}
0.665 (+/-0.315) for {'C': 10.0, 'kernel': 'linear'}
0.664 (+/-0.316) for {'C': 100.0, 'kernel': 'linear'}
0.612 (+/-0.241) for {'C': 1000.0, 'kernel': 'linear'}
0.611 (+/-0.239) for {'C': 10000.0, 'kernel': 'linear'}
0.611 (+/-0.245) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6996794871794872
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.675 (+/-0.359) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.659 (+/-0.359) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.660 (+/-0.357) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.663 (+/-0.357) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.670 (+/-0.353) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.691 (+/-0.357) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.666 (+/-0.354) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.640 (+/-0.299) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.037528415818012e-06}
0.669 (+/-0.363) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.05681750921366e-06}
0.693 (+/-0.412) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.076465213629836e-06}
0.695 (+/-0.410) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.667 (+/-0.356) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.659 (+/-0.359) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.660 (+/-0.357) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.659 (+/-0.358) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.662 (+/-0.356) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.681 (+/-0.355) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.664 (+/-0.356) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.648 (+/-0.315) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 1.037528415818012e-06}
0.669 (+/-0.363) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 1.05681750921366e-06}
0.693 (+/-0.412) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 1.076465213629836e-06}
0.674 (+/-0.359) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.668 (+/-0.355) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.659 (+/-0.359) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.660 (+/-0.357) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.654 (+/-0.360) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.664 (+/-0.355) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.689 (+/-0.358) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.660 (+/-0.357) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.635 (+/-0.293) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 1.037528415818012e-06}
0.669 (+/-0.363) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 1.05681750921366e-06}
0.693 (+/-0.412) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 1.076465213629836e-06}
0.674 (+/-0.359) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.668 (+/-0.355) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.652 (+/-0.361) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.658 (+/-0.358) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.654 (+/-0.360) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.664 (+/-0.355) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.681 (+/-0.355) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.660 (+/-0.357) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.665 (+/-0.366) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 1.037528415818012e-06}
0.669 (+/-0.363) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 1.05681750921366e-06}
0.668 (+/-0.362) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 1.076465213629836e-06}
0.674 (+/-0.359) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.664 (+/-0.357) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.652 (+/-0.361) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.656 (+/-0.358) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.654 (+/-0.360) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.664 (+/-0.355) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.656 (+/-0.292) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.634 (+/-0.288) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.665 (+/-0.366) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 1.037528415818012e-06}
0.669 (+/-0.363) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 1.05681750921366e-06}
0.668 (+/-0.362) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 1.076465213629836e-06}
0.674 (+/-0.359) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.664 (+/-0.357) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.651 (+/-0.362) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.656 (+/-0.358) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.652 (+/-0.362) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.662 (+/-0.356) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.656 (+/-0.292) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.635 (+/-0.287) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.635 (+/-0.293) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.037528415818012e-06}
0.668 (+/-0.364) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.05681750921366e-06}
0.672 (+/-0.361) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.076465213629836e-06}
0.674 (+/-0.359) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.664 (+/-0.357) for {'C': 1342764.9611378654, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.657 (+/-0.359) for {'C': 1342764.9611378654, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.656 (+/-0.358) for {'C': 1342764.9611378654, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.651 (+/-0.363) for {'C': 1342764.9611378654, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.666 (+/-0.355) for {'C': 1342764.9611378654, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.681 (+/-0.355) for {'C': 1342764.9611378654, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.637 (+/-0.288) for {'C': 1342764.9611378654, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.665 (+/-0.366) for {'C': 1342764.9611378654, 'kernel': 'rbf', 'gamma': 1.037528415818012e-06}
0.668 (+/-0.364) for {'C': 1342764.9611378654, 'kernel': 'rbf', 'gamma': 1.05681750921366e-06}
0.668 (+/-0.362) for {'C': 1342764.9611378654, 'kernel': 'rbf', 'gamma': 1.076465213629836e-06}
0.674 (+/-0.359) for {'C': 1342764.9611378654, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.664 (+/-0.357) for {'C': 1367728.8255958504, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.657 (+/-0.359) for {'C': 1367728.8255958504, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.656 (+/-0.358) for {'C': 1367728.8255958504, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.651 (+/-0.363) for {'C': 1367728.8255958504, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.666 (+/-0.355) for {'C': 1367728.8255958504, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.651 (+/-0.293) for {'C': 1367728.8255958504, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.640 (+/-0.290) for {'C': 1367728.8255958504, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.665 (+/-0.366) for {'C': 1367728.8255958504, 'kernel': 'rbf', 'gamma': 1.037528415818012e-06}
0.673 (+/-0.362) for {'C': 1367728.8255958504, 'kernel': 'rbf', 'gamma': 1.05681750921366e-06}
0.670 (+/-0.362) for {'C': 1367728.8255958504, 'kernel': 'rbf', 'gamma': 1.076465213629836e-06}
0.674 (+/-0.359) for {'C': 1367728.8255958504, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.667 (+/-0.354) for {'C': 1393156.8029453037, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.657 (+/-0.359) for {'C': 1393156.8029453037, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.656 (+/-0.358) for {'C': 1393156.8029453037, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.651 (+/-0.363) for {'C': 1393156.8029453037, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.666 (+/-0.355) for {'C': 1393156.8029453037, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.651 (+/-0.293) for {'C': 1393156.8029453037, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.640 (+/-0.290) for {'C': 1393156.8029453037, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.648 (+/-0.315) for {'C': 1393156.8029453037, 'kernel': 'rbf', 'gamma': 1.037528415818012e-06}
0.673 (+/-0.362) for {'C': 1393156.8029453037, 'kernel': 'rbf', 'gamma': 1.05681750921366e-06}
0.670 (+/-0.362) for {'C': 1393156.8029453037, 'kernel': 'rbf', 'gamma': 1.076465213629836e-06}
0.674 (+/-0.359) for {'C': 1393156.8029453037, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.665 (+/-0.356) for {'C': 1419057.5216890923, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.657 (+/-0.359) for {'C': 1419057.5216890923, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.657 (+/-0.357) for {'C': 1419057.5216890923, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.655 (+/-0.362) for {'C': 1419057.5216890923, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.666 (+/-0.355) for {'C': 1419057.5216890923, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.651 (+/-0.293) for {'C': 1419057.5216890923, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.637 (+/-0.288) for {'C': 1419057.5216890923, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.648 (+/-0.315) for {'C': 1419057.5216890923, 'kernel': 'rbf', 'gamma': 1.037528415818012e-06}
0.673 (+/-0.362) for {'C': 1419057.5216890923, 'kernel': 'rbf', 'gamma': 1.05681750921366e-06}
0.675 (+/-0.360) for {'C': 1419057.5216890923, 'kernel': 'rbf', 'gamma': 1.076465213629836e-06}
0.674 (+/-0.359) for {'C': 1419057.5216890923, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.665 (+/-0.356) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.657 (+/-0.359) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.657 (+/-0.357) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.655 (+/-0.362) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.666 (+/-0.355) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.651 (+/-0.293) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.640 (+/-0.290) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.650 (+/-0.314) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.037528415818012e-06}
0.673 (+/-0.362) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.05681750921366e-06}
0.672 (+/-0.361) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.076465213629836e-06}
0.674 (+/-0.359) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.764 (+/-0.421) for {'C': 1.0, 'kernel': 'linear'}
0.643 (+/-0.306) for {'C': 10.0, 'kernel': 'linear'}
0.630 (+/-0.312) for {'C': 100.0, 'kernel': 'linear'}
0.620 (+/-0.321) for {'C': 1000.0, 'kernel': 'linear'}
0.618 (+/-0.322) for {'C': 10000.0, 'kernel': 'linear'}
0.624 (+/-0.319) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.20 0.17 0.18 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.697 (+/-0.302) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.696 (+/-0.302) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.697 (+/-0.303) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.680 (+/-0.292) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.697 (+/-0.305) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.698 (+/-0.306) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.697 (+/-0.305) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.696 (+/-0.305) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.037528415818012e-06}
0.697 (+/-0.307) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.05681750921366e-06}
0.697 (+/-0.307) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.076465213629836e-06}
0.697 (+/-0.307) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.697 (+/-0.302) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.696 (+/-0.302) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.697 (+/-0.303) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.680 (+/-0.292) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.697 (+/-0.304) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.698 (+/-0.306) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.697 (+/-0.304) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.697 (+/-0.306) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 1.037528415818012e-06}
0.697 (+/-0.307) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 1.05681750921366e-06}
0.697 (+/-0.307) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 1.076465213629836e-06}
0.697 (+/-0.307) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.697 (+/-0.302) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.696 (+/-0.302) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.697 (+/-0.303) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.680 (+/-0.292) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.697 (+/-0.304) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.698 (+/-0.306) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.697 (+/-0.304) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.696 (+/-0.305) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 1.037528415818012e-06}
0.697 (+/-0.307) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 1.05681750921366e-06}
0.697 (+/-0.307) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 1.076465213629836e-06}
0.697 (+/-0.307) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.697 (+/-0.302) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.680 (+/-0.290) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.696 (+/-0.303) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.680 (+/-0.292) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.697 (+/-0.304) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.698 (+/-0.306) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.697 (+/-0.304) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.697 (+/-0.306) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 1.037528415818012e-06}
0.697 (+/-0.307) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 1.05681750921366e-06}
0.697 (+/-0.306) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 1.076465213629836e-06}
0.697 (+/-0.307) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.696 (+/-0.302) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.680 (+/-0.290) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.696 (+/-0.303) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.680 (+/-0.292) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.697 (+/-0.304) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.697 (+/-0.306) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.696 (+/-0.304) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.697 (+/-0.306) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 1.037528415818012e-06}
0.697 (+/-0.307) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 1.05681750921366e-06}
0.697 (+/-0.306) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 1.076465213629836e-06}
0.697 (+/-0.307) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.696 (+/-0.302) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.679 (+/-0.290) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.696 (+/-0.303) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.680 (+/-0.292) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.697 (+/-0.304) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.697 (+/-0.306) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.696 (+/-0.304) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.696 (+/-0.305) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.037528415818012e-06}
0.697 (+/-0.307) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.05681750921366e-06}
0.697 (+/-0.307) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.076465213629836e-06}
0.697 (+/-0.307) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.696 (+/-0.302) for {'C': 1342764.9611378654, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.696 (+/-0.302) for {'C': 1342764.9611378654, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.696 (+/-0.303) for {'C': 1342764.9611378654, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.680 (+/-0.292) for {'C': 1342764.9611378654, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.697 (+/-0.304) for {'C': 1342764.9611378654, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.698 (+/-0.306) for {'C': 1342764.9611378654, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.697 (+/-0.304) for {'C': 1342764.9611378654, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.697 (+/-0.306) for {'C': 1342764.9611378654, 'kernel': 'rbf', 'gamma': 1.037528415818012e-06}
0.697 (+/-0.307) for {'C': 1342764.9611378654, 'kernel': 'rbf', 'gamma': 1.05681750921366e-06}
0.697 (+/-0.306) for {'C': 1342764.9611378654, 'kernel': 'rbf', 'gamma': 1.076465213629836e-06}
0.697 (+/-0.307) for {'C': 1342764.9611378654, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.696 (+/-0.302) for {'C': 1367728.8255958504, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.696 (+/-0.302) for {'C': 1367728.8255958504, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.696 (+/-0.303) for {'C': 1367728.8255958504, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.680 (+/-0.292) for {'C': 1367728.8255958504, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.697 (+/-0.304) for {'C': 1367728.8255958504, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.697 (+/-0.305) for {'C': 1367728.8255958504, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.697 (+/-0.305) for {'C': 1367728.8255958504, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.697 (+/-0.306) for {'C': 1367728.8255958504, 'kernel': 'rbf', 'gamma': 1.037528415818012e-06}
0.697 (+/-0.307) for {'C': 1367728.8255958504, 'kernel': 'rbf', 'gamma': 1.05681750921366e-06}
0.697 (+/-0.307) for {'C': 1367728.8255958504, 'kernel': 'rbf', 'gamma': 1.076465213629836e-06}
0.697 (+/-0.307) for {'C': 1367728.8255958504, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.697 (+/-0.303) for {'C': 1393156.8029453037, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.696 (+/-0.302) for {'C': 1393156.8029453037, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.696 (+/-0.303) for {'C': 1393156.8029453037, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.680 (+/-0.292) for {'C': 1393156.8029453037, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.697 (+/-0.304) for {'C': 1393156.8029453037, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.697 (+/-0.305) for {'C': 1393156.8029453037, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.697 (+/-0.305) for {'C': 1393156.8029453037, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.697 (+/-0.306) for {'C': 1393156.8029453037, 'kernel': 'rbf', 'gamma': 1.037528415818012e-06}
0.697 (+/-0.307) for {'C': 1393156.8029453037, 'kernel': 'rbf', 'gamma': 1.05681750921366e-06}
0.697 (+/-0.307) for {'C': 1393156.8029453037, 'kernel': 'rbf', 'gamma': 1.076465213629836e-06}
0.697 (+/-0.307) for {'C': 1393156.8029453037, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.697 (+/-0.302) for {'C': 1419057.5216890923, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.696 (+/-0.302) for {'C': 1419057.5216890923, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.696 (+/-0.303) for {'C': 1419057.5216890923, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.680 (+/-0.292) for {'C': 1419057.5216890923, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.697 (+/-0.304) for {'C': 1419057.5216890923, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.697 (+/-0.305) for {'C': 1419057.5216890923, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.697 (+/-0.304) for {'C': 1419057.5216890923, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.697 (+/-0.306) for {'C': 1419057.5216890923, 'kernel': 'rbf', 'gamma': 1.037528415818012e-06}
0.697 (+/-0.307) for {'C': 1419057.5216890923, 'kernel': 'rbf', 'gamma': 1.05681750921366e-06}
0.697 (+/-0.307) for {'C': 1419057.5216890923, 'kernel': 'rbf', 'gamma': 1.076465213629836e-06}
0.697 (+/-0.307) for {'C': 1419057.5216890923, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.697 (+/-0.302) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.696 (+/-0.302) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 9.289663867799355e-07}
0.696 (+/-0.303) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 9.462371613657919e-07}
0.680 (+/-0.292) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 9.638290236239714e-07}
0.697 (+/-0.304) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.697 (+/-0.305) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.697 (+/-0.305) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.697 (+/-0.306) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.037528415818012e-06}
0.697 (+/-0.307) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.05681750921366e-06}
0.697 (+/-0.307) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.076465213629836e-06}
0.697 (+/-0.307) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.0964781961431857e-06}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.683 (+/-0.296) for {'C': 1.0, 'kernel': 'linear'}
0.664 (+/-0.314) for {'C': 10.0, 'kernel': 'linear'}
0.660 (+/-0.316) for {'C': 100.0, 'kernel': 'linear'}
0.612 (+/-0.241) for {'C': 1000.0, 'kernel': 'linear'}
0.611 (+/-0.239) for {'C': 10000.0, 'kernel': 'linear'}
0.611 (+/-0.245) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.11 0.17 0.13 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6979146054909721
第1轮loop中,tunemodel函数得到的最优参数是: ([{'C': array([1224616.19926505, 1229136.17306017, 1233672.82976633,
1238226.23095899, 1242796.43844091, 1247383.51424294,
1251987.52062488, 1256608.52007633, 1261246.57531754,
1265901.74930024, 1270574.10520854]), 'kernel': ['rbf'], 'gamma': array([9.81747943e-07, 9.85371507e-07, 9.89008445e-07, 9.92658807e-07,
9.96322642e-07, 1.00000000e-06, 1.00369093e-06, 1.00739548e-06,
1.01111371e-06, 1.01484566e-06, 1.01859139e-06])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}], {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}, 0.6996794871794872)
这是第1次迭代微调C和gamma。
第1次迭代,得到delta: [70873.22431346 0. ]
执行tunemodel()函数前,使用的grid search parameter是:
[{'C': array([1224616.19926505, 1229136.17306017, 1233672.82976633,
1238226.23095899, 1242796.43844091, 1247383.51424294,
1251987.52062488, 1256608.52007633, 1261246.57531754,
1265901.74930024, 1270574.10520854]), 'kernel': ['rbf'], 'gamma': array([9.81747943e-07, 9.85371507e-07, 9.89008445e-07, 9.92658807e-07,
9.96322642e-07, 1.00000000e-06, 1.00369093e-06, 1.00739548e-06,
1.01111371e-06, 1.01484566e-06, 1.01859139e-06])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.723 (+/-0.417) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.748 (+/-0.449) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 9.853715068586198e-07}
0.748 (+/-0.449) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 9.890084450210653e-07}
0.748 (+/-0.449) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 9.926588068710316e-07}
0.748 (+/-0.449) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 9.963226419544168e-07}
0.798 (+/-0.437) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.748 (+/-0.449) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 1.003690930920098e-06}
0.798 (+/-0.437) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 1.0073954848112503e-06}
0.748 (+/-0.449) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 1.0111137119549073e-06}
0.798 (+/-0.437) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 1.0148456628180941e-06}
0.798 (+/-0.437) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.723 (+/-0.417) for {'C': 1229136.1730601697, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.748 (+/-0.449) for {'C': 1229136.1730601697, 'kernel': 'rbf', 'gamma': 9.853715068586198e-07}
0.748 (+/-0.449) for {'C': 1229136.1730601697, 'kernel': 'rbf', 'gamma': 9.890084450210653e-07}
0.748 (+/-0.449) for {'C': 1229136.1730601697, 'kernel': 'rbf', 'gamma': 9.926588068710316e-07}
0.748 (+/-0.449) for {'C': 1229136.1730601697, 'kernel': 'rbf', 'gamma': 9.963226419544168e-07}
0.798 (+/-0.437) for {'C': 1229136.1730601697, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.748 (+/-0.449) for {'C': 1229136.1730601697, 'kernel': 'rbf', 'gamma': 1.003690930920098e-06}
0.798 (+/-0.437) for {'C': 1229136.1730601697, 'kernel': 'rbf', 'gamma': 1.0073954848112503e-06}
0.748 (+/-0.449) for {'C': 1229136.1730601697, 'kernel': 'rbf', 'gamma': 1.0111137119549073e-06}
0.798 (+/-0.437) for {'C': 1229136.1730601697, 'kernel': 'rbf', 'gamma': 1.0148456628180941e-06}
0.798 (+/-0.437) for {'C': 1229136.1730601697, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.723 (+/-0.417) for {'C': 1233672.8297663252, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.748 (+/-0.449) for {'C': 1233672.8297663252, 'kernel': 'rbf', 'gamma': 9.853715068586198e-07}
0.748 (+/-0.449) for {'C': 1233672.8297663252, 'kernel': 'rbf', 'gamma': 9.890084450210653e-07}
0.748 (+/-0.449) for {'C': 1233672.8297663252, 'kernel': 'rbf', 'gamma': 9.926588068710316e-07}
0.798 (+/-0.437) for {'C': 1233672.8297663252, 'kernel': 'rbf', 'gamma': 9.963226419544168e-07}
0.798 (+/-0.437) for {'C': 1233672.8297663252, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.798 (+/-0.437) for {'C': 1233672.8297663252, 'kernel': 'rbf', 'gamma': 1.003690930920098e-06}
0.798 (+/-0.437) for {'C': 1233672.8297663252, 'kernel': 'rbf', 'gamma': 1.0073954848112503e-06}
0.748 (+/-0.449) for {'C': 1233672.8297663252, 'kernel': 'rbf', 'gamma': 1.0111137119549073e-06}
0.798 (+/-0.437) for {'C': 1233672.8297663252, 'kernel': 'rbf', 'gamma': 1.0148456628180941e-06}
0.798 (+/-0.437) for {'C': 1233672.8297663252, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.723 (+/-0.417) for {'C': 1238226.2309589945, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.748 (+/-0.449) for {'C': 1238226.2309589945, 'kernel': 'rbf', 'gamma': 9.853715068586198e-07}
0.748 (+/-0.449) for {'C': 1238226.2309589945, 'kernel': 'rbf', 'gamma': 9.890084450210653e-07}
0.748 (+/-0.449) for {'C': 1238226.2309589945, 'kernel': 'rbf', 'gamma': 9.926588068710316e-07}
0.748 (+/-0.449) for {'C': 1238226.2309589945, 'kernel': 'rbf', 'gamma': 9.963226419544168e-07}
0.798 (+/-0.437) for {'C': 1238226.2309589945, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.798 (+/-0.437) for {'C': 1238226.2309589945, 'kernel': 'rbf', 'gamma': 1.003690930920098e-06}
0.798 (+/-0.437) for {'C': 1238226.2309589945, 'kernel': 'rbf', 'gamma': 1.0073954848112503e-06}
0.748 (+/-0.449) for {'C': 1238226.2309589945, 'kernel': 'rbf', 'gamma': 1.0111137119549073e-06}
0.798 (+/-0.437) for {'C': 1238226.2309589945, 'kernel': 'rbf', 'gamma': 1.0148456628180941e-06}
0.798 (+/-0.437) for {'C': 1238226.2309589945, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.723 (+/-0.417) for {'C': 1242796.4384409143, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.748 (+/-0.449) for {'C': 1242796.4384409143, 'kernel': 'rbf', 'gamma': 9.853715068586198e-07}
0.748 (+/-0.449) for {'C': 1242796.4384409143, 'kernel': 'rbf', 'gamma': 9.890084450210653e-07}
0.748 (+/-0.449) for {'C': 1242796.4384409143, 'kernel': 'rbf', 'gamma': 9.926588068710316e-07}
0.748 (+/-0.449) for {'C': 1242796.4384409143, 'kernel': 'rbf', 'gamma': 9.963226419544168e-07}
0.798 (+/-0.437) for {'C': 1242796.4384409143, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.798 (+/-0.437) for {'C': 1242796.4384409143, 'kernel': 'rbf', 'gamma': 1.003690930920098e-06}
0.798 (+/-0.437) for {'C': 1242796.4384409143, 'kernel': 'rbf', 'gamma': 1.0073954848112503e-06}
0.748 (+/-0.449) for {'C': 1242796.4384409143, 'kernel': 'rbf', 'gamma': 1.0111137119549073e-06}
0.798 (+/-0.437) for {'C': 1242796.4384409143, 'kernel': 'rbf', 'gamma': 1.0148456628180941e-06}
0.798 (+/-0.437) for {'C': 1242796.4384409143, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.748 (+/-0.449) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.748 (+/-0.449) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 9.853715068586198e-07}
0.748 (+/-0.449) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 9.890084450210653e-07}
0.748 (+/-0.449) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 9.926588068710316e-07}
0.748 (+/-0.449) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 9.963226419544168e-07}
0.798 (+/-0.437) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.798 (+/-0.437) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 1.003690930920098e-06}
0.798 (+/-0.437) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 1.0073954848112503e-06}
0.748 (+/-0.449) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 1.0111137119549073e-06}
0.798 (+/-0.437) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 1.0148456628180941e-06}
0.798 (+/-0.437) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.723 (+/-0.417) for {'C': 1251987.520624883, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.748 (+/-0.449) for {'C': 1251987.520624883, 'kernel': 'rbf', 'gamma': 9.853715068586198e-07}
0.748 (+/-0.449) for {'C': 1251987.520624883, 'kernel': 'rbf', 'gamma': 9.890084450210653e-07}
0.748 (+/-0.449) for {'C': 1251987.520624883, 'kernel': 'rbf', 'gamma': 9.926588068710316e-07}
0.798 (+/-0.437) for {'C': 1251987.520624883, 'kernel': 'rbf', 'gamma': 9.963226419544168e-07}
0.798 (+/-0.437) for {'C': 1251987.520624883, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.798 (+/-0.437) for {'C': 1251987.520624883, 'kernel': 'rbf', 'gamma': 1.003690930920098e-06}
0.798 (+/-0.437) for {'C': 1251987.520624883, 'kernel': 'rbf', 'gamma': 1.0073954848112503e-06}
0.748 (+/-0.449) for {'C': 1251987.520624883, 'kernel': 'rbf', 'gamma': 1.0111137119549073e-06}
0.798 (+/-0.437) for {'C': 1251987.520624883, 'kernel': 'rbf', 'gamma': 1.0148456628180941e-06}
0.798 (+/-0.437) for {'C': 1251987.520624883, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.723 (+/-0.417) for {'C': 1256608.520076331, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.748 (+/-0.449) for {'C': 1256608.520076331, 'kernel': 'rbf', 'gamma': 9.853715068586198e-07}
0.748 (+/-0.449) for {'C': 1256608.520076331, 'kernel': 'rbf', 'gamma': 9.890084450210653e-07}
0.748 (+/-0.449) for {'C': 1256608.520076331, 'kernel': 'rbf', 'gamma': 9.926588068710316e-07}
0.748 (+/-0.449) for {'C': 1256608.520076331, 'kernel': 'rbf', 'gamma': 9.963226419544168e-07}
0.798 (+/-0.437) for {'C': 1256608.520076331, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.798 (+/-0.437) for {'C': 1256608.520076331, 'kernel': 'rbf', 'gamma': 1.003690930920098e-06}
0.798 (+/-0.437) for {'C': 1256608.520076331, 'kernel': 'rbf', 'gamma': 1.0073954848112503e-06}
0.748 (+/-0.449) for {'C': 1256608.520076331, 'kernel': 'rbf', 'gamma': 1.0111137119549073e-06}
0.798 (+/-0.437) for {'C': 1256608.520076331, 'kernel': 'rbf', 'gamma': 1.0148456628180941e-06}
0.798 (+/-0.437) for {'C': 1256608.520076331, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.723 (+/-0.417) for {'C': 1261246.5753175393, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.748 (+/-0.449) for {'C': 1261246.5753175393, 'kernel': 'rbf', 'gamma': 9.853715068586198e-07}
0.748 (+/-0.449) for {'C': 1261246.5753175393, 'kernel': 'rbf', 'gamma': 9.890084450210653e-07}
0.748 (+/-0.449) for {'C': 1261246.5753175393, 'kernel': 'rbf', 'gamma': 9.926588068710316e-07}
0.748 (+/-0.449) for {'C': 1261246.5753175393, 'kernel': 'rbf', 'gamma': 9.963226419544168e-07}
0.798 (+/-0.437) for {'C': 1261246.5753175393, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.798 (+/-0.437) for {'C': 1261246.5753175393, 'kernel': 'rbf', 'gamma': 1.003690930920098e-06}
0.798 (+/-0.437) for {'C': 1261246.5753175393, 'kernel': 'rbf', 'gamma': 1.0073954848112503e-06}
0.748 (+/-0.449) for {'C': 1261246.5753175393, 'kernel': 'rbf', 'gamma': 1.0111137119549073e-06}
0.798 (+/-0.437) for {'C': 1261246.5753175393, 'kernel': 'rbf', 'gamma': 1.0148456628180941e-06}
0.798 (+/-0.437) for {'C': 1261246.5753175393, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.723 (+/-0.417) for {'C': 1265901.7493002433, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.748 (+/-0.449) for {'C': 1265901.7493002433, 'kernel': 'rbf', 'gamma': 9.853715068586198e-07}
0.748 (+/-0.449) for {'C': 1265901.7493002433, 'kernel': 'rbf', 'gamma': 9.890084450210653e-07}
0.748 (+/-0.449) for {'C': 1265901.7493002433, 'kernel': 'rbf', 'gamma': 9.926588068710316e-07}
0.748 (+/-0.449) for {'C': 1265901.7493002433, 'kernel': 'rbf', 'gamma': 9.963226419544168e-07}
0.798 (+/-0.437) for {'C': 1265901.7493002433, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.798 (+/-0.437) for {'C': 1265901.7493002433, 'kernel': 'rbf', 'gamma': 1.003690930920098e-06}
0.798 (+/-0.437) for {'C': 1265901.7493002433, 'kernel': 'rbf', 'gamma': 1.0073954848112503e-06}
0.748 (+/-0.449) for {'C': 1265901.7493002433, 'kernel': 'rbf', 'gamma': 1.0111137119549073e-06}
0.798 (+/-0.437) for {'C': 1265901.7493002433, 'kernel': 'rbf', 'gamma': 1.0148456628180941e-06}
0.798 (+/-0.437) for {'C': 1265901.7493002433, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.723 (+/-0.417) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.748 (+/-0.449) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 9.853715068586198e-07}
0.748 (+/-0.449) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 9.890084450210653e-07}
0.748 (+/-0.449) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 9.926588068710316e-07}
0.748 (+/-0.449) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 9.963226419544168e-07}
0.798 (+/-0.437) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.798 (+/-0.437) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 1.003690930920098e-06}
0.798 (+/-0.437) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 1.0073954848112503e-06}
0.748 (+/-0.449) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 1.0111137119549073e-06}
0.798 (+/-0.437) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 1.0148456628180941e-06}
0.798 (+/-0.437) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'linear'}
0.673 (+/-0.330) for {'C': 10.0, 'kernel': 'linear'}
0.646 (+/-0.317) for {'C': 100.0, 'kernel': 'linear'}
0.620 (+/-0.321) for {'C': 1000.0, 'kernel': 'linear'}
0.618 (+/-0.322) for {'C': 10000.0, 'kernel': 'linear'}
0.624 (+/-0.319) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.666 (+/-0.316) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.666 (+/-0.316) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 9.853715068586198e-07}
0.666 (+/-0.316) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 9.890084450210653e-07}
0.666 (+/-0.316) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 9.926588068710316e-07}
0.666 (+/-0.316) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 9.963226419544168e-07}
0.683 (+/-0.296) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.666 (+/-0.316) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 1.003690930920098e-06}
0.683 (+/-0.296) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 1.0073954848112503e-06}
0.666 (+/-0.316) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 1.0111137119549073e-06}
0.683 (+/-0.296) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 1.0148456628180941e-06}
0.683 (+/-0.296) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.666 (+/-0.316) for {'C': 1229136.1730601697, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.666 (+/-0.316) for {'C': 1229136.1730601697, 'kernel': 'rbf', 'gamma': 9.853715068586198e-07}
0.666 (+/-0.316) for {'C': 1229136.1730601697, 'kernel': 'rbf', 'gamma': 9.890084450210653e-07}
0.666 (+/-0.316) for {'C': 1229136.1730601697, 'kernel': 'rbf', 'gamma': 9.926588068710316e-07}
0.666 (+/-0.316) for {'C': 1229136.1730601697, 'kernel': 'rbf', 'gamma': 9.963226419544168e-07}
0.683 (+/-0.296) for {'C': 1229136.1730601697, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.666 (+/-0.316) for {'C': 1229136.1730601697, 'kernel': 'rbf', 'gamma': 1.003690930920098e-06}
0.683 (+/-0.296) for {'C': 1229136.1730601697, 'kernel': 'rbf', 'gamma': 1.0073954848112503e-06}
0.666 (+/-0.316) for {'C': 1229136.1730601697, 'kernel': 'rbf', 'gamma': 1.0111137119549073e-06}
0.683 (+/-0.296) for {'C': 1229136.1730601697, 'kernel': 'rbf', 'gamma': 1.0148456628180941e-06}
0.683 (+/-0.296) for {'C': 1229136.1730601697, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.666 (+/-0.316) for {'C': 1233672.8297663252, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.666 (+/-0.316) for {'C': 1233672.8297663252, 'kernel': 'rbf', 'gamma': 9.853715068586198e-07}
0.666 (+/-0.316) for {'C': 1233672.8297663252, 'kernel': 'rbf', 'gamma': 9.890084450210653e-07}
0.666 (+/-0.316) for {'C': 1233672.8297663252, 'kernel': 'rbf', 'gamma': 9.926588068710316e-07}
0.683 (+/-0.296) for {'C': 1233672.8297663252, 'kernel': 'rbf', 'gamma': 9.963226419544168e-07}
0.683 (+/-0.296) for {'C': 1233672.8297663252, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.683 (+/-0.296) for {'C': 1233672.8297663252, 'kernel': 'rbf', 'gamma': 1.003690930920098e-06}
0.683 (+/-0.296) for {'C': 1233672.8297663252, 'kernel': 'rbf', 'gamma': 1.0073954848112503e-06}
0.666 (+/-0.316) for {'C': 1233672.8297663252, 'kernel': 'rbf', 'gamma': 1.0111137119549073e-06}
0.683 (+/-0.296) for {'C': 1233672.8297663252, 'kernel': 'rbf', 'gamma': 1.0148456628180941e-06}
0.683 (+/-0.296) for {'C': 1233672.8297663252, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.666 (+/-0.316) for {'C': 1238226.2309589945, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.666 (+/-0.316) for {'C': 1238226.2309589945, 'kernel': 'rbf', 'gamma': 9.853715068586198e-07}
0.666 (+/-0.316) for {'C': 1238226.2309589945, 'kernel': 'rbf', 'gamma': 9.890084450210653e-07}
0.666 (+/-0.316) for {'C': 1238226.2309589945, 'kernel': 'rbf', 'gamma': 9.926588068710316e-07}
0.666 (+/-0.316) for {'C': 1238226.2309589945, 'kernel': 'rbf', 'gamma': 9.963226419544168e-07}
0.683 (+/-0.296) for {'C': 1238226.2309589945, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.683 (+/-0.296) for {'C': 1238226.2309589945, 'kernel': 'rbf', 'gamma': 1.003690930920098e-06}
0.683 (+/-0.296) for {'C': 1238226.2309589945, 'kernel': 'rbf', 'gamma': 1.0073954848112503e-06}
0.666 (+/-0.316) for {'C': 1238226.2309589945, 'kernel': 'rbf', 'gamma': 1.0111137119549073e-06}
0.683 (+/-0.296) for {'C': 1238226.2309589945, 'kernel': 'rbf', 'gamma': 1.0148456628180941e-06}
0.683 (+/-0.296) for {'C': 1238226.2309589945, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.666 (+/-0.316) for {'C': 1242796.4384409143, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.666 (+/-0.316) for {'C': 1242796.4384409143, 'kernel': 'rbf', 'gamma': 9.853715068586198e-07}
0.666 (+/-0.316) for {'C': 1242796.4384409143, 'kernel': 'rbf', 'gamma': 9.890084450210653e-07}
0.666 (+/-0.316) for {'C': 1242796.4384409143, 'kernel': 'rbf', 'gamma': 9.926588068710316e-07}
0.666 (+/-0.316) for {'C': 1242796.4384409143, 'kernel': 'rbf', 'gamma': 9.963226419544168e-07}
0.700 (+/-0.309) for {'C': 1242796.4384409143, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.683 (+/-0.296) for {'C': 1242796.4384409143, 'kernel': 'rbf', 'gamma': 1.003690930920098e-06}
0.683 (+/-0.296) for {'C': 1242796.4384409143, 'kernel': 'rbf', 'gamma': 1.0073954848112503e-06}
0.666 (+/-0.316) for {'C': 1242796.4384409143, 'kernel': 'rbf', 'gamma': 1.0111137119549073e-06}
0.683 (+/-0.296) for {'C': 1242796.4384409143, 'kernel': 'rbf', 'gamma': 1.0148456628180941e-06}
0.683 (+/-0.296) for {'C': 1242796.4384409143, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.666 (+/-0.316) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.666 (+/-0.316) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 9.853715068586198e-07}
0.666 (+/-0.316) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 9.890084450210653e-07}
0.666 (+/-0.316) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 9.926588068710316e-07}
0.666 (+/-0.316) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 9.963226419544168e-07}
0.700 (+/-0.309) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.683 (+/-0.296) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 1.003690930920098e-06}
0.683 (+/-0.296) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 1.0073954848112503e-06}
0.666 (+/-0.316) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 1.0111137119549073e-06}
0.683 (+/-0.296) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 1.0148456628180941e-06}
0.683 (+/-0.296) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.666 (+/-0.316) for {'C': 1251987.520624883, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.666 (+/-0.316) for {'C': 1251987.520624883, 'kernel': 'rbf', 'gamma': 9.853715068586198e-07}
0.666 (+/-0.316) for {'C': 1251987.520624883, 'kernel': 'rbf', 'gamma': 9.890084450210653e-07}
0.666 (+/-0.316) for {'C': 1251987.520624883, 'kernel': 'rbf', 'gamma': 9.926588068710316e-07}
0.683 (+/-0.296) for {'C': 1251987.520624883, 'kernel': 'rbf', 'gamma': 9.963226419544168e-07}
0.700 (+/-0.309) for {'C': 1251987.520624883, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.683 (+/-0.296) for {'C': 1251987.520624883, 'kernel': 'rbf', 'gamma': 1.003690930920098e-06}
0.683 (+/-0.296) for {'C': 1251987.520624883, 'kernel': 'rbf', 'gamma': 1.0073954848112503e-06}
0.666 (+/-0.316) for {'C': 1251987.520624883, 'kernel': 'rbf', 'gamma': 1.0111137119549073e-06}
0.683 (+/-0.296) for {'C': 1251987.520624883, 'kernel': 'rbf', 'gamma': 1.0148456628180941e-06}
0.683 (+/-0.296) for {'C': 1251987.520624883, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.666 (+/-0.316) for {'C': 1256608.520076331, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.666 (+/-0.316) for {'C': 1256608.520076331, 'kernel': 'rbf', 'gamma': 9.853715068586198e-07}
0.666 (+/-0.316) for {'C': 1256608.520076331, 'kernel': 'rbf', 'gamma': 9.890084450210653e-07}
0.666 (+/-0.316) for {'C': 1256608.520076331, 'kernel': 'rbf', 'gamma': 9.926588068710316e-07}
0.666 (+/-0.316) for {'C': 1256608.520076331, 'kernel': 'rbf', 'gamma': 9.963226419544168e-07}
0.700 (+/-0.309) for {'C': 1256608.520076331, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.683 (+/-0.296) for {'C': 1256608.520076331, 'kernel': 'rbf', 'gamma': 1.003690930920098e-06}
0.683 (+/-0.296) for {'C': 1256608.520076331, 'kernel': 'rbf', 'gamma': 1.0073954848112503e-06}
0.666 (+/-0.316) for {'C': 1256608.520076331, 'kernel': 'rbf', 'gamma': 1.0111137119549073e-06}
0.683 (+/-0.296) for {'C': 1256608.520076331, 'kernel': 'rbf', 'gamma': 1.0148456628180941e-06}
0.683 (+/-0.296) for {'C': 1256608.520076331, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.666 (+/-0.316) for {'C': 1261246.5753175393, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.666 (+/-0.316) for {'C': 1261246.5753175393, 'kernel': 'rbf', 'gamma': 9.853715068586198e-07}
0.666 (+/-0.316) for {'C': 1261246.5753175393, 'kernel': 'rbf', 'gamma': 9.890084450210653e-07}
0.666 (+/-0.316) for {'C': 1261246.5753175393, 'kernel': 'rbf', 'gamma': 9.926588068710316e-07}
0.666 (+/-0.316) for {'C': 1261246.5753175393, 'kernel': 'rbf', 'gamma': 9.963226419544168e-07}
0.700 (+/-0.309) for {'C': 1261246.5753175393, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.683 (+/-0.296) for {'C': 1261246.5753175393, 'kernel': 'rbf', 'gamma': 1.003690930920098e-06}
0.683 (+/-0.296) for {'C': 1261246.5753175393, 'kernel': 'rbf', 'gamma': 1.0073954848112503e-06}
0.666 (+/-0.316) for {'C': 1261246.5753175393, 'kernel': 'rbf', 'gamma': 1.0111137119549073e-06}
0.683 (+/-0.296) for {'C': 1261246.5753175393, 'kernel': 'rbf', 'gamma': 1.0148456628180941e-06}
0.683 (+/-0.296) for {'C': 1261246.5753175393, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.666 (+/-0.316) for {'C': 1265901.7493002433, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.666 (+/-0.316) for {'C': 1265901.7493002433, 'kernel': 'rbf', 'gamma': 9.853715068586198e-07}
0.666 (+/-0.316) for {'C': 1265901.7493002433, 'kernel': 'rbf', 'gamma': 9.890084450210653e-07}
0.666 (+/-0.316) for {'C': 1265901.7493002433, 'kernel': 'rbf', 'gamma': 9.926588068710316e-07}
0.666 (+/-0.316) for {'C': 1265901.7493002433, 'kernel': 'rbf', 'gamma': 9.963226419544168e-07}
0.700 (+/-0.309) for {'C': 1265901.7493002433, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.683 (+/-0.296) for {'C': 1265901.7493002433, 'kernel': 'rbf', 'gamma': 1.003690930920098e-06}
0.683 (+/-0.296) for {'C': 1265901.7493002433, 'kernel': 'rbf', 'gamma': 1.0073954848112503e-06}
0.666 (+/-0.316) for {'C': 1265901.7493002433, 'kernel': 'rbf', 'gamma': 1.0111137119549073e-06}
0.683 (+/-0.296) for {'C': 1265901.7493002433, 'kernel': 'rbf', 'gamma': 1.0148456628180941e-06}
0.683 (+/-0.296) for {'C': 1265901.7493002433, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.666 (+/-0.316) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.666 (+/-0.316) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 9.853715068586198e-07}
0.666 (+/-0.316) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 9.890084450210653e-07}
0.666 (+/-0.316) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 9.926588068710316e-07}
0.666 (+/-0.316) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 9.963226419544168e-07}
0.700 (+/-0.309) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.683 (+/-0.296) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 1.003690930920098e-06}
0.683 (+/-0.296) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 1.0073954848112503e-06}
0.666 (+/-0.316) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 1.0111137119549073e-06}
0.683 (+/-0.296) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 1.0148456628180941e-06}
0.683 (+/-0.296) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'linear'}
0.665 (+/-0.315) for {'C': 10.0, 'kernel': 'linear'}
0.664 (+/-0.316) for {'C': 100.0, 'kernel': 'linear'}
0.612 (+/-0.241) for {'C': 1000.0, 'kernel': 'linear'}
0.611 (+/-0.239) for {'C': 10000.0, 'kernel': 'linear'}
0.611 (+/-0.245) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 1242796.4384409143, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6996794871794872
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.662 (+/-0.356) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.659 (+/-0.359) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 9.853715068586198e-07}
0.665 (+/-0.356) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 9.890084450210653e-07}
0.676 (+/-0.357) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 9.926588068710316e-07}
0.676 (+/-0.357) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 9.963226419544168e-07}
0.681 (+/-0.355) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.685 (+/-0.360) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 1.003690930920098e-06}
0.650 (+/-0.294) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 1.0073954848112503e-06}
0.664 (+/-0.355) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 1.0111137119549073e-06}
0.663 (+/-0.358) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 1.0148456628180941e-06}
0.664 (+/-0.356) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.662 (+/-0.356) for {'C': 1229136.1730601697, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.663 (+/-0.357) for {'C': 1229136.1730601697, 'kernel': 'rbf', 'gamma': 9.853715068586198e-07}
0.665 (+/-0.356) for {'C': 1229136.1730601697, 'kernel': 'rbf', 'gamma': 9.890084450210653e-07}
0.676 (+/-0.357) for {'C': 1229136.1730601697, 'kernel': 'rbf', 'gamma': 9.926588068710316e-07}
0.676 (+/-0.357) for {'C': 1229136.1730601697, 'kernel': 'rbf', 'gamma': 9.963226419544168e-07}
0.681 (+/-0.355) for {'C': 1229136.1730601697, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.685 (+/-0.360) for {'C': 1229136.1730601697, 'kernel': 'rbf', 'gamma': 1.003690930920098e-06}
0.650 (+/-0.294) for {'C': 1229136.1730601697, 'kernel': 'rbf', 'gamma': 1.0073954848112503e-06}
0.664 (+/-0.355) for {'C': 1229136.1730601697, 'kernel': 'rbf', 'gamma': 1.0111137119549073e-06}
0.663 (+/-0.358) for {'C': 1229136.1730601697, 'kernel': 'rbf', 'gamma': 1.0148456628180941e-06}
0.662 (+/-0.357) for {'C': 1229136.1730601697, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.662 (+/-0.356) for {'C': 1233672.8297663252, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.663 (+/-0.357) for {'C': 1233672.8297663252, 'kernel': 'rbf', 'gamma': 9.853715068586198e-07}
0.665 (+/-0.356) for {'C': 1233672.8297663252, 'kernel': 'rbf', 'gamma': 9.890084450210653e-07}
0.676 (+/-0.357) for {'C': 1233672.8297663252, 'kernel': 'rbf', 'gamma': 9.926588068710316e-07}
0.676 (+/-0.357) for {'C': 1233672.8297663252, 'kernel': 'rbf', 'gamma': 9.963226419544168e-07}
0.681 (+/-0.355) for {'C': 1233672.8297663252, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.685 (+/-0.360) for {'C': 1233672.8297663252, 'kernel': 'rbf', 'gamma': 1.003690930920098e-06}
0.677 (+/-0.358) for {'C': 1233672.8297663252, 'kernel': 'rbf', 'gamma': 1.0073954848112503e-06}
0.664 (+/-0.355) for {'C': 1233672.8297663252, 'kernel': 'rbf', 'gamma': 1.0111137119549073e-06}
0.665 (+/-0.357) for {'C': 1233672.8297663252, 'kernel': 'rbf', 'gamma': 1.0148456628180941e-06}
0.662 (+/-0.357) for {'C': 1233672.8297663252, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.662 (+/-0.356) for {'C': 1238226.2309589945, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.659 (+/-0.359) for {'C': 1238226.2309589945, 'kernel': 'rbf', 'gamma': 9.853715068586198e-07}
0.663 (+/-0.357) for {'C': 1238226.2309589945, 'kernel': 'rbf', 'gamma': 9.890084450210653e-07}
0.676 (+/-0.357) for {'C': 1238226.2309589945, 'kernel': 'rbf', 'gamma': 9.926588068710316e-07}
0.676 (+/-0.357) for {'C': 1238226.2309589945, 'kernel': 'rbf', 'gamma': 9.963226419544168e-07}
0.681 (+/-0.355) for {'C': 1238226.2309589945, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.685 (+/-0.360) for {'C': 1238226.2309589945, 'kernel': 'rbf', 'gamma': 1.003690930920098e-06}
0.675 (+/-0.358) for {'C': 1238226.2309589945, 'kernel': 'rbf', 'gamma': 1.0073954848112503e-06}
0.639 (+/-0.286) for {'C': 1238226.2309589945, 'kernel': 'rbf', 'gamma': 1.0111137119549073e-06}
0.665 (+/-0.357) for {'C': 1238226.2309589945, 'kernel': 'rbf', 'gamma': 1.0148456628180941e-06}
0.662 (+/-0.357) for {'C': 1238226.2309589945, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.664 (+/-0.355) for {'C': 1242796.4384409143, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.659 (+/-0.359) for {'C': 1242796.4384409143, 'kernel': 'rbf', 'gamma': 9.853715068586198e-07}
0.663 (+/-0.357) for {'C': 1242796.4384409143, 'kernel': 'rbf', 'gamma': 9.890084450210653e-07}
0.674 (+/-0.360) for {'C': 1242796.4384409143, 'kernel': 'rbf', 'gamma': 9.926588068710316e-07}
0.676 (+/-0.357) for {'C': 1242796.4384409143, 'kernel': 'rbf', 'gamma': 9.963226419544168e-07}
0.681 (+/-0.355) for {'C': 1242796.4384409143, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.685 (+/-0.360) for {'C': 1242796.4384409143, 'kernel': 'rbf', 'gamma': 1.003690930920098e-06}
0.675 (+/-0.358) for {'C': 1242796.4384409143, 'kernel': 'rbf', 'gamma': 1.0073954848112503e-06}
0.639 (+/-0.286) for {'C': 1242796.4384409143, 'kernel': 'rbf', 'gamma': 1.0111137119549073e-06}
0.663 (+/-0.358) for {'C': 1242796.4384409143, 'kernel': 'rbf', 'gamma': 1.0148456628180941e-06}
0.662 (+/-0.357) for {'C': 1242796.4384409143, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.664 (+/-0.355) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.659 (+/-0.359) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 9.853715068586198e-07}
0.663 (+/-0.357) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 9.890084450210653e-07}
0.674 (+/-0.360) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 9.926588068710316e-07}
0.676 (+/-0.357) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 9.963226419544168e-07}
0.689 (+/-0.358) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.685 (+/-0.360) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 1.003690930920098e-06}
0.650 (+/-0.294) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 1.0073954848112503e-06}
0.639 (+/-0.286) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 1.0111137119549073e-06}
0.638 (+/-0.290) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 1.0148456628180941e-06}
0.660 (+/-0.357) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.664 (+/-0.355) for {'C': 1251987.520624883, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.661 (+/-0.358) for {'C': 1251987.520624883, 'kernel': 'rbf', 'gamma': 9.853715068586198e-07}
0.663 (+/-0.357) for {'C': 1251987.520624883, 'kernel': 'rbf', 'gamma': 9.890084450210653e-07}
0.674 (+/-0.360) for {'C': 1251987.520624883, 'kernel': 'rbf', 'gamma': 9.926588068710316e-07}
0.676 (+/-0.357) for {'C': 1251987.520624883, 'kernel': 'rbf', 'gamma': 9.963226419544168e-07}
0.681 (+/-0.355) for {'C': 1251987.520624883, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.685 (+/-0.360) for {'C': 1251987.520624883, 'kernel': 'rbf', 'gamma': 1.003690930920098e-06}
0.647 (+/-0.294) for {'C': 1251987.520624883, 'kernel': 'rbf', 'gamma': 1.0073954848112503e-06}
0.639 (+/-0.286) for {'C': 1251987.520624883, 'kernel': 'rbf', 'gamma': 1.0111137119549073e-06}
0.663 (+/-0.358) for {'C': 1251987.520624883, 'kernel': 'rbf', 'gamma': 1.0148456628180941e-06}
0.660 (+/-0.357) for {'C': 1251987.520624883, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.664 (+/-0.355) for {'C': 1256608.520076331, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.659 (+/-0.359) for {'C': 1256608.520076331, 'kernel': 'rbf', 'gamma': 9.853715068586198e-07}
0.663 (+/-0.357) for {'C': 1256608.520076331, 'kernel': 'rbf', 'gamma': 9.890084450210653e-07}
0.674 (+/-0.360) for {'C': 1256608.520076331, 'kernel': 'rbf', 'gamma': 9.926588068710316e-07}
0.676 (+/-0.357) for {'C': 1256608.520076331, 'kernel': 'rbf', 'gamma': 9.963226419544168e-07}
0.681 (+/-0.355) for {'C': 1256608.520076331, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.685 (+/-0.360) for {'C': 1256608.520076331, 'kernel': 'rbf', 'gamma': 1.003690930920098e-06}
0.652 (+/-0.294) for {'C': 1256608.520076331, 'kernel': 'rbf', 'gamma': 1.0073954848112503e-06}
0.639 (+/-0.286) for {'C': 1256608.520076331, 'kernel': 'rbf', 'gamma': 1.0111137119549073e-06}
0.663 (+/-0.358) for {'C': 1256608.520076331, 'kernel': 'rbf', 'gamma': 1.0148456628180941e-06}
0.660 (+/-0.357) for {'C': 1256608.520076331, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.664 (+/-0.355) for {'C': 1261246.5753175393, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.659 (+/-0.359) for {'C': 1261246.5753175393, 'kernel': 'rbf', 'gamma': 9.853715068586198e-07}
0.663 (+/-0.357) for {'C': 1261246.5753175393, 'kernel': 'rbf', 'gamma': 9.890084450210653e-07}
0.676 (+/-0.357) for {'C': 1261246.5753175393, 'kernel': 'rbf', 'gamma': 9.926588068710316e-07}
0.676 (+/-0.357) for {'C': 1261246.5753175393, 'kernel': 'rbf', 'gamma': 9.963226419544168e-07}
0.681 (+/-0.355) for {'C': 1261246.5753175393, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.685 (+/-0.360) for {'C': 1261246.5753175393, 'kernel': 'rbf', 'gamma': 1.003690930920098e-06}
0.650 (+/-0.294) for {'C': 1261246.5753175393, 'kernel': 'rbf', 'gamma': 1.0073954848112503e-06}
0.639 (+/-0.286) for {'C': 1261246.5753175393, 'kernel': 'rbf', 'gamma': 1.0111137119549073e-06}
0.663 (+/-0.358) for {'C': 1261246.5753175393, 'kernel': 'rbf', 'gamma': 1.0148456628180941e-06}
0.660 (+/-0.357) for {'C': 1261246.5753175393, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.664 (+/-0.355) for {'C': 1265901.7493002433, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.659 (+/-0.359) for {'C': 1265901.7493002433, 'kernel': 'rbf', 'gamma': 9.853715068586198e-07}
0.663 (+/-0.357) for {'C': 1265901.7493002433, 'kernel': 'rbf', 'gamma': 9.890084450210653e-07}
0.674 (+/-0.360) for {'C': 1265901.7493002433, 'kernel': 'rbf', 'gamma': 9.926588068710316e-07}
0.676 (+/-0.357) for {'C': 1265901.7493002433, 'kernel': 'rbf', 'gamma': 9.963226419544168e-07}
0.681 (+/-0.355) for {'C': 1265901.7493002433, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.677 (+/-0.358) for {'C': 1265901.7493002433, 'kernel': 'rbf', 'gamma': 1.003690930920098e-06}
0.650 (+/-0.294) for {'C': 1265901.7493002433, 'kernel': 'rbf', 'gamma': 1.0073954848112503e-06}
0.639 (+/-0.286) for {'C': 1265901.7493002433, 'kernel': 'rbf', 'gamma': 1.0111137119549073e-06}
0.663 (+/-0.358) for {'C': 1265901.7493002433, 'kernel': 'rbf', 'gamma': 1.0148456628180941e-06}
0.660 (+/-0.357) for {'C': 1265901.7493002433, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.664 (+/-0.355) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.659 (+/-0.359) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 9.853715068586198e-07}
0.663 (+/-0.357) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 9.890084450210653e-07}
0.674 (+/-0.360) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 9.926588068710316e-07}
0.676 (+/-0.357) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 9.963226419544168e-07}
0.681 (+/-0.355) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.677 (+/-0.358) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 1.003690930920098e-06}
0.650 (+/-0.294) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 1.0073954848112503e-06}
0.639 (+/-0.286) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 1.0111137119549073e-06}
0.663 (+/-0.358) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 1.0148456628180941e-06}
0.660 (+/-0.357) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.764 (+/-0.421) for {'C': 1.0, 'kernel': 'linear'}
0.643 (+/-0.306) for {'C': 10.0, 'kernel': 'linear'}
0.630 (+/-0.312) for {'C': 100.0, 'kernel': 'linear'}
0.620 (+/-0.321) for {'C': 1000.0, 'kernel': 'linear'}
0.618 (+/-0.322) for {'C': 10000.0, 'kernel': 'linear'}
0.624 (+/-0.319) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.20 0.17 0.18 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.697 (+/-0.304) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.696 (+/-0.304) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 9.853715068586198e-07}
0.697 (+/-0.304) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 9.890084450210653e-07}
0.697 (+/-0.306) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 9.926588068710316e-07}
0.697 (+/-0.306) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 9.963226419544168e-07}
0.698 (+/-0.306) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.698 (+/-0.306) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 1.003690930920098e-06}
0.697 (+/-0.304) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 1.0073954848112503e-06}
0.697 (+/-0.304) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 1.0111137119549073e-06}
0.697 (+/-0.305) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 1.0148456628180941e-06}
0.697 (+/-0.304) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.697 (+/-0.304) for {'C': 1229136.1730601697, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.697 (+/-0.304) for {'C': 1229136.1730601697, 'kernel': 'rbf', 'gamma': 9.853715068586198e-07}
0.697 (+/-0.304) for {'C': 1229136.1730601697, 'kernel': 'rbf', 'gamma': 9.890084450210653e-07}
0.697 (+/-0.306) for {'C': 1229136.1730601697, 'kernel': 'rbf', 'gamma': 9.926588068710316e-07}
0.697 (+/-0.306) for {'C': 1229136.1730601697, 'kernel': 'rbf', 'gamma': 9.963226419544168e-07}
0.698 (+/-0.306) for {'C': 1229136.1730601697, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.698 (+/-0.306) for {'C': 1229136.1730601697, 'kernel': 'rbf', 'gamma': 1.003690930920098e-06}
0.697 (+/-0.304) for {'C': 1229136.1730601697, 'kernel': 'rbf', 'gamma': 1.0073954848112503e-06}
0.697 (+/-0.304) for {'C': 1229136.1730601697, 'kernel': 'rbf', 'gamma': 1.0111137119549073e-06}
0.697 (+/-0.305) for {'C': 1229136.1730601697, 'kernel': 'rbf', 'gamma': 1.0148456628180941e-06}
0.697 (+/-0.304) for {'C': 1229136.1730601697, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.697 (+/-0.304) for {'C': 1233672.8297663252, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.697 (+/-0.304) for {'C': 1233672.8297663252, 'kernel': 'rbf', 'gamma': 9.853715068586198e-07}
0.697 (+/-0.304) for {'C': 1233672.8297663252, 'kernel': 'rbf', 'gamma': 9.890084450210653e-07}
0.697 (+/-0.306) for {'C': 1233672.8297663252, 'kernel': 'rbf', 'gamma': 9.926588068710316e-07}
0.697 (+/-0.306) for {'C': 1233672.8297663252, 'kernel': 'rbf', 'gamma': 9.963226419544168e-07}
0.698 (+/-0.306) for {'C': 1233672.8297663252, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.698 (+/-0.306) for {'C': 1233672.8297663252, 'kernel': 'rbf', 'gamma': 1.003690930920098e-06}
0.697 (+/-0.305) for {'C': 1233672.8297663252, 'kernel': 'rbf', 'gamma': 1.0073954848112503e-06}
0.697 (+/-0.304) for {'C': 1233672.8297663252, 'kernel': 'rbf', 'gamma': 1.0111137119549073e-06}
0.697 (+/-0.305) for {'C': 1233672.8297663252, 'kernel': 'rbf', 'gamma': 1.0148456628180941e-06}
0.697 (+/-0.304) for {'C': 1233672.8297663252, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.697 (+/-0.304) for {'C': 1238226.2309589945, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.696 (+/-0.304) for {'C': 1238226.2309589945, 'kernel': 'rbf', 'gamma': 9.853715068586198e-07}
0.697 (+/-0.304) for {'C': 1238226.2309589945, 'kernel': 'rbf', 'gamma': 9.890084450210653e-07}
0.697 (+/-0.306) for {'C': 1238226.2309589945, 'kernel': 'rbf', 'gamma': 9.926588068710316e-07}
0.697 (+/-0.306) for {'C': 1238226.2309589945, 'kernel': 'rbf', 'gamma': 9.963226419544168e-07}
0.698 (+/-0.306) for {'C': 1238226.2309589945, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.698 (+/-0.306) for {'C': 1238226.2309589945, 'kernel': 'rbf', 'gamma': 1.003690930920098e-06}
0.697 (+/-0.304) for {'C': 1238226.2309589945, 'kernel': 'rbf', 'gamma': 1.0073954848112503e-06}
0.697 (+/-0.304) for {'C': 1238226.2309589945, 'kernel': 'rbf', 'gamma': 1.0111137119549073e-06}
0.697 (+/-0.305) for {'C': 1238226.2309589945, 'kernel': 'rbf', 'gamma': 1.0148456628180941e-06}
0.697 (+/-0.304) for {'C': 1238226.2309589945, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.697 (+/-0.304) for {'C': 1242796.4384409143, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.696 (+/-0.304) for {'C': 1242796.4384409143, 'kernel': 'rbf', 'gamma': 9.853715068586198e-07}
0.697 (+/-0.304) for {'C': 1242796.4384409143, 'kernel': 'rbf', 'gamma': 9.890084450210653e-07}
0.697 (+/-0.305) for {'C': 1242796.4384409143, 'kernel': 'rbf', 'gamma': 9.926588068710316e-07}
0.697 (+/-0.306) for {'C': 1242796.4384409143, 'kernel': 'rbf', 'gamma': 9.963226419544168e-07}
0.698 (+/-0.306) for {'C': 1242796.4384409143, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.698 (+/-0.306) for {'C': 1242796.4384409143, 'kernel': 'rbf', 'gamma': 1.003690930920098e-06}
0.697 (+/-0.304) for {'C': 1242796.4384409143, 'kernel': 'rbf', 'gamma': 1.0073954848112503e-06}
0.697 (+/-0.304) for {'C': 1242796.4384409143, 'kernel': 'rbf', 'gamma': 1.0111137119549073e-06}
0.697 (+/-0.305) for {'C': 1242796.4384409143, 'kernel': 'rbf', 'gamma': 1.0148456628180941e-06}
0.697 (+/-0.304) for {'C': 1242796.4384409143, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.697 (+/-0.304) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.696 (+/-0.304) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 9.853715068586198e-07}
0.697 (+/-0.304) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 9.890084450210653e-07}
0.697 (+/-0.305) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 9.926588068710316e-07}
0.697 (+/-0.306) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 9.963226419544168e-07}
0.698 (+/-0.306) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.698 (+/-0.306) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 1.003690930920098e-06}
0.697 (+/-0.304) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 1.0073954848112503e-06}
0.697 (+/-0.304) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 1.0111137119549073e-06}
0.697 (+/-0.305) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 1.0148456628180941e-06}
0.697 (+/-0.304) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.697 (+/-0.304) for {'C': 1251987.520624883, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.697 (+/-0.304) for {'C': 1251987.520624883, 'kernel': 'rbf', 'gamma': 9.853715068586198e-07}
0.697 (+/-0.304) for {'C': 1251987.520624883, 'kernel': 'rbf', 'gamma': 9.890084450210653e-07}
0.697 (+/-0.305) for {'C': 1251987.520624883, 'kernel': 'rbf', 'gamma': 9.926588068710316e-07}
0.697 (+/-0.306) for {'C': 1251987.520624883, 'kernel': 'rbf', 'gamma': 9.963226419544168e-07}
0.698 (+/-0.306) for {'C': 1251987.520624883, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.698 (+/-0.306) for {'C': 1251987.520624883, 'kernel': 'rbf', 'gamma': 1.003690930920098e-06}
0.697 (+/-0.304) for {'C': 1251987.520624883, 'kernel': 'rbf', 'gamma': 1.0073954848112503e-06}
0.697 (+/-0.304) for {'C': 1251987.520624883, 'kernel': 'rbf', 'gamma': 1.0111137119549073e-06}
0.697 (+/-0.305) for {'C': 1251987.520624883, 'kernel': 'rbf', 'gamma': 1.0148456628180941e-06}
0.697 (+/-0.304) for {'C': 1251987.520624883, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.697 (+/-0.304) for {'C': 1256608.520076331, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.696 (+/-0.304) for {'C': 1256608.520076331, 'kernel': 'rbf', 'gamma': 9.853715068586198e-07}
0.697 (+/-0.304) for {'C': 1256608.520076331, 'kernel': 'rbf', 'gamma': 9.890084450210653e-07}
0.697 (+/-0.305) for {'C': 1256608.520076331, 'kernel': 'rbf', 'gamma': 9.926588068710316e-07}
0.697 (+/-0.306) for {'C': 1256608.520076331, 'kernel': 'rbf', 'gamma': 9.963226419544168e-07}
0.698 (+/-0.306) for {'C': 1256608.520076331, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.698 (+/-0.306) for {'C': 1256608.520076331, 'kernel': 'rbf', 'gamma': 1.003690930920098e-06}
0.697 (+/-0.305) for {'C': 1256608.520076331, 'kernel': 'rbf', 'gamma': 1.0073954848112503e-06}
0.697 (+/-0.304) for {'C': 1256608.520076331, 'kernel': 'rbf', 'gamma': 1.0111137119549073e-06}
0.697 (+/-0.305) for {'C': 1256608.520076331, 'kernel': 'rbf', 'gamma': 1.0148456628180941e-06}
0.697 (+/-0.304) for {'C': 1256608.520076331, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.697 (+/-0.304) for {'C': 1261246.5753175393, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.696 (+/-0.304) for {'C': 1261246.5753175393, 'kernel': 'rbf', 'gamma': 9.853715068586198e-07}
0.697 (+/-0.304) for {'C': 1261246.5753175393, 'kernel': 'rbf', 'gamma': 9.890084450210653e-07}
0.697 (+/-0.306) for {'C': 1261246.5753175393, 'kernel': 'rbf', 'gamma': 9.926588068710316e-07}
0.697 (+/-0.306) for {'C': 1261246.5753175393, 'kernel': 'rbf', 'gamma': 9.963226419544168e-07}
0.698 (+/-0.306) for {'C': 1261246.5753175393, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.698 (+/-0.306) for {'C': 1261246.5753175393, 'kernel': 'rbf', 'gamma': 1.003690930920098e-06}
0.697 (+/-0.304) for {'C': 1261246.5753175393, 'kernel': 'rbf', 'gamma': 1.0073954848112503e-06}
0.697 (+/-0.304) for {'C': 1261246.5753175393, 'kernel': 'rbf', 'gamma': 1.0111137119549073e-06}
0.697 (+/-0.305) for {'C': 1261246.5753175393, 'kernel': 'rbf', 'gamma': 1.0148456628180941e-06}
0.697 (+/-0.304) for {'C': 1261246.5753175393, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.697 (+/-0.304) for {'C': 1265901.7493002433, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.696 (+/-0.304) for {'C': 1265901.7493002433, 'kernel': 'rbf', 'gamma': 9.853715068586198e-07}
0.697 (+/-0.304) for {'C': 1265901.7493002433, 'kernel': 'rbf', 'gamma': 9.890084450210653e-07}
0.697 (+/-0.305) for {'C': 1265901.7493002433, 'kernel': 'rbf', 'gamma': 9.926588068710316e-07}
0.697 (+/-0.306) for {'C': 1265901.7493002433, 'kernel': 'rbf', 'gamma': 9.963226419544168e-07}
0.698 (+/-0.306) for {'C': 1265901.7493002433, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.697 (+/-0.306) for {'C': 1265901.7493002433, 'kernel': 'rbf', 'gamma': 1.003690930920098e-06}
0.697 (+/-0.304) for {'C': 1265901.7493002433, 'kernel': 'rbf', 'gamma': 1.0073954848112503e-06}
0.697 (+/-0.304) for {'C': 1265901.7493002433, 'kernel': 'rbf', 'gamma': 1.0111137119549073e-06}
0.697 (+/-0.305) for {'C': 1265901.7493002433, 'kernel': 'rbf', 'gamma': 1.0148456628180941e-06}
0.697 (+/-0.304) for {'C': 1265901.7493002433, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.697 (+/-0.304) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 9.81747943019985e-07}
0.696 (+/-0.304) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 9.853715068586198e-07}
0.697 (+/-0.304) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 9.890084450210653e-07}
0.697 (+/-0.305) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 9.926588068710316e-07}
0.697 (+/-0.306) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 9.963226419544168e-07}
0.698 (+/-0.306) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.697 (+/-0.306) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 1.003690930920098e-06}
0.697 (+/-0.304) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 1.0073954848112503e-06}
0.697 (+/-0.304) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 1.0111137119549073e-06}
0.697 (+/-0.305) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 1.0148456628180941e-06}
0.697 (+/-0.304) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 1.0185913880541167e-06}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.683 (+/-0.296) for {'C': 1.0, 'kernel': 'linear'}
0.664 (+/-0.314) for {'C': 10.0, 'kernel': 'linear'}
0.660 (+/-0.316) for {'C': 100.0, 'kernel': 'linear'}
0.612 (+/-0.241) for {'C': 1000.0, 'kernel': 'linear'}
0.611 (+/-0.239) for {'C': 10000.0, 'kernel': 'linear'}
0.611 (+/-0.245) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.11 0.17 0.13 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.697753833786792
第2轮loop中,tunemodel函数得到的最优参数是: ([{'C': array([1238226.23095899, 1239138.92597054, 1240052.29372844,
1240966.33472858, 1241881.0494672 , 1242796.43844091,
1243712.5021467 , 1244629.2410819 , 1245546.65574423,
1246464.74663176, 1247383.51424294]), 'kernel': ['rbf'], 'gamma': array([9.96322642e-07, 9.97057030e-07, 9.97791960e-07, 9.98527431e-07,
9.99263444e-07, 1.00000000e-06, 1.00073710e-06, 1.00147474e-06,
1.00221293e-06, 1.00295166e-06, 1.00369093e-06])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}], {'C': 1242796.4384409143, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}, 0.6996794871794872)
这是第2次迭代微调C和gamma。
第2次迭代,得到delta: [4587.07580203 0. ]
SVC模型clf_op_iter的参数是: {'kernel': 'rbf', 'decision_function_shape': 'ovr', 'class_weight': {-1: 15}, 'tol': 0.001, 'gamma': 1.0000000000000002e-06, 'random_state': 0, 'cache_size': 200, 'verbose': False, 'degree': 3, 'C': 1242796.4384409143, 'probability': False, 'shrinking': True, 'coef0': 0.0, 'max_iter': -1}
训练模型SVC对训练样本的预测准确率: 0.9461374911410347
测试集中,预测为舞弊样本的有: (array([ 8, 10, 11, 12, 13, 14, 21, 22, 24, 25, 26,
27, 28, 30, 33, 34, 35, 36, 37, 39, 40, 42,
43, 44, 46, 47, 48, 49, 50, 51, 52, 53, 56,
58, 59, 60, 61, 62, 64, 65, 66, 67, 68, 69,
70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 82,
88, 90, 91, 97, 98, 101, 102, 109, 111, 112, 113,
114, 115, 116, 117, 118, 119, 121, 127, 129, 136, 142,
143, 144, 145, 146, 147, 148, 150, 151, 153, 157, 158,
163, 164, 165, 166, 176, 177, 178, 179, 180, 181, 182,
183, 184, 185, 186, 192, 193, 195, 197, 200, 201, 202,
205, 207, 208, 209, 211, 212, 213, 214, 215, 216, 219,
225, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238,
239, 243, 244, 245, 246, 247, 248, 250, 251, 252, 253,
254, 255, 256, 257, 258, 259, 260, 261, 264, 268, 269,
270, 276, 277, 279, 281, 284, 286, 287, 288, 289, 300,
301, 302, 306, 316, 318, 322, 323, 324, 325, 330, 331,
332, 333, 338, 339, 340, 342, 344, 346, 347, 348, 349,
351, 352, 353, 354, 358, 359, 360, 361, 362, 363, 364,
365, 366, 370, 383, 384, 385, 386, 391, 392, 393, 394,
395, 396, 397, 398, 399, 403, 404, 414, 419, 420, 421,
422, 423, 426, 427, 428, 429, 430, 431, 432, 433, 434,
438, 439, 440, 441, 442, 443, 444, 445, 447, 449, 450,
454, 455, 457, 458, 459, 460, 461, 462, 463, 464, 465,
466, 467, 468, 469, 471, 472, 473, 474, 475, 476, 477,
478, 479, 481, 482, 483, 484, 485, 486, 487, 488, 489,
490, 491, 495, 496, 498, 499, 500, 501, 502, 503, 504,
505, 506, 507, 508, 509, 511, 513, 515, 516, 521, 529,
532, 534, 535, 540, 543, 544, 545, 546, 547, 549, 555,
558, 560, 561, 562, 563, 564, 565, 566, 567, 568, 571,
572, 573, 584, 587, 589, 590, 593, 604, 605, 611, 614,
615, 616, 617, 618, 619, 620, 621, 622, 623, 624, 629,
630, 635, 641, 642, 643, 651, 652, 653, 655, 656, 657,
658, 659, 660, 661, 662, 666, 667, 668, 669, 675, 676,
677, 678, 685, 686, 687, 688, 693, 694, 695, 696, 697,
699, 701, 702, 703, 706, 709, 716, 717, 718, 719, 720,
721, 722, 723, 724, 725, 729, 735, 736, 742, 743, 750,
751, 752, 753, 754, 756, 757, 758, 759, 760, 761, 769,
774, 775, 776, 777, 778, 786, 787, 788, 789, 790, 791,
792, 793, 794, 795, 796, 797, 813, 814, 816, 817, 818,
819, 820, 825, 826, 829, 830, 840, 841, 845, 851, 852,
853, 854, 855, 856, 857, 858, 859, 860, 861, 862, 864,
872, 874, 875, 876, 877, 878, 879, 880, 881, 882, 883,
884, 885, 897, 898, 900, 902, 903, 904, 905, 906, 907,
910, 911, 912, 913, 914, 917, 919, 927, 928, 929, 930,
931, 932, 933, 934, 935, 947, 951, 952, 953, 954, 955,
956, 957, 958, 959, 960, 961, 962, 963, 964, 965, 966,
967, 977, 978, 979, 980, 981, 982, 983, 984, 985, 995,
998, 999, 1000, 1002, 1003, 1004, 1005, 1006, 1007, 1008, 1009,
1012, 1013, 1014, 1015, 1016, 1017, 1020, 1021, 1022, 1030, 1037,
1041, 1049, 1050, 1051, 1055, 1056, 1057, 1060, 1061, 1065, 1067,
1068, 1074, 1080, 1081, 1082, 1085, 1086, 1089, 1095, 1096, 1097,
1098, 1105, 1106, 1107, 1110, 1112, 1113, 1114, 1117, 1118, 1119,
1121, 1122, 1123, 1124, 1125, 1127, 1129, 1131, 1132, 1134, 1135,
1136, 1137, 1140, 1141, 1142, 1144, 1148, 1152, 1153, 1154, 1156,
1158, 1160, 1164, 1167, 1168, 1170, 1171, 1175, 1180, 1181, 1183,
1184, 1188, 1189, 1191, 1194, 1200, 1204, 1205, 1208, 1211, 1215,
1216, 1217, 1218, 1219, 1222, 1230, 1232, 1233, 1234, 1235, 1236,
1237, 1238, 1239, 1240, 1241, 1242, 1243, 1244, 1246, 1247, 1248,
1249, 1250, 1251, 1252, 1254, 1255, 1256], dtype=int64),)
测试集中,实际为舞弊样本的有: (array([1246, 1247, 1248, 1249, 1250, 1251, 1252, 1253, 1254, 1255, 1256],
dtype=int64),)
预测的舞弊样本数目: 645
训练模型SVC对测试样本的预测准确率: 0.5393338058114813
以上是第33次特征筛选。
第33次特征筛选,AUC值是: 0.6997300452356633
X_train_iter_svc.shape is: (1257, 19)
X_test_iter_svc.shape is: (1257, 19)
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.647 (+/-0.387) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.656 (+/-0.218) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.614 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.682 (+/-0.294) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.17 0.17 0.17 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6817292233489982
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.643 (+/-0.202) for {'C': 1.0, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.632 (+/-0.309) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.17 0.17 0.17 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.698 (+/-0.306) for {'C': 1.0, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.661 (+/-0.314) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.17 0.17 0.17 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6979151207848958
RBF的粗grid search最佳超参: {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001} RBF的粗grid search最高分值: 0.6817292233489982
linear的粗grid search最佳超参: {'C': 1.0, 'kernel': 'linear'} linear的粗grid search最高分值: 0.6979151207848958
粗grid search得到的parameter是:
[{'C': array([1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02, 1.e+03, 1.e+04, 1.e+05,
1.e+06, 1.e+07, 1.e+08]), 'kernel': ['rbf'], 'gamma': array([1.e-08, 1.e-07, 1.e-06, 1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01,
1.e+00, 1.e+01, 1.e+02])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.797 (+/-0.492) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.848 (+/-0.459) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.665 (+/-0.225) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.631 (+/-0.319) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.848 (+/-0.459) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.656 (+/-0.218) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.312) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.645 (+/-0.388) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.848 (+/-0.459) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.656 (+/-0.218) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.310) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.647 (+/-0.387) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.848 (+/-0.459) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.656 (+/-0.218) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.690 (+/-0.299) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.639 (+/-0.314) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.622 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.647 (+/-0.387) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.848 (+/-0.460) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.651 (+/-0.211) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.690 (+/-0.299) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.639 (+/-0.314) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.622 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.647 (+/-0.387) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.823 (+/-0.451) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.685 (+/-0.297) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.668 (+/-0.290) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.633 (+/-0.312) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.622 (+/-0.319) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.622 (+/-0.319) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.647 (+/-0.387) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.848 (+/-0.460) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.773 (+/-0.474) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.601 (+/-0.176) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.628 (+/-0.312) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.623 (+/-0.316) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.323) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.622 (+/-0.319) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.647 (+/-0.387) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.701 (+/-0.417) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.656 (+/-0.413) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.658 (+/-0.293) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.624 (+/-0.316) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.619 (+/-0.322) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.614 (+/-0.327) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.622 (+/-0.319) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.647 (+/-0.387) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.690 (+/-0.300) for {'C': 1.0, 'kernel': 'linear'}
0.673 (+/-0.294) for {'C': 10.0, 'kernel': 'linear'}
0.648 (+/-0.315) for {'C': 100.0, 'kernel': 'linear'}
0.632 (+/-0.309) for {'C': 1000.0, 'kernel': 'linear'}
0.622 (+/-0.319) for {'C': 10000.0, 'kernel': 'linear'}
0.618 (+/-0.322) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.642 (+/-0.236) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.658 (+/-0.217) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.698 (+/-0.307) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.006) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.658 (+/-0.217) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.682 (+/-0.294) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.632 (+/-0.225) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.240) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.006) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.658 (+/-0.217) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.682 (+/-0.294) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.699 (+/-0.308) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.587 (+/-0.233) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.006) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.658 (+/-0.217) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.682 (+/-0.294) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.698 (+/-0.307) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.662 (+/-0.316) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.240) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.006) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.683 (+/-0.296) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.698 (+/-0.306) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.698 (+/-0.307) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.662 (+/-0.316) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.587 (+/-0.233) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.613 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.006) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.683 (+/-0.296) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.698 (+/-0.306) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.681 (+/-0.294) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.661 (+/-0.315) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.636 (+/-0.323) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.587 (+/-0.233) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.613 (+/-0.239) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.006) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.683 (+/-0.296) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.667 (+/-0.316) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.696 (+/-0.301) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.661 (+/-0.311) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.658 (+/-0.308) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.611 (+/-0.237) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.587 (+/-0.233) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.613 (+/-0.239) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.006) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.674 (+/-0.324) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.616 (+/-0.326) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.698 (+/-0.308) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.659 (+/-0.307) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.633 (+/-0.321) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.587 (+/-0.235) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.587 (+/-0.233) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.613 (+/-0.239) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.006) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.699 (+/-0.307) for {'C': 1.0, 'kernel': 'linear'}
0.698 (+/-0.307) for {'C': 10.0, 'kernel': 'linear'}
0.664 (+/-0.316) for {'C': 100.0, 'kernel': 'linear'}
0.661 (+/-0.314) for {'C': 1000.0, 'kernel': 'linear'}
0.636 (+/-0.322) for {'C': 10000.0, 'kernel': 'linear'}
0.611 (+/-0.239) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.14 0.17 0.15 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6985566617198451
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.582 (+/-0.184) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.671 (+/-0.291) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.645 (+/-0.386) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.680 (+/-0.295) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.185) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.618 (+/-0.322) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.680 (+/-0.295) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.605 (+/-0.165) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.643 (+/-0.306) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.645 (+/-0.388) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.680 (+/-0.295) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.601 (+/-0.159) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.658 (+/-0.302) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.647 (+/-0.387) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.680 (+/-0.295) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.604 (+/-0.159) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.658 (+/-0.302) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.627 (+/-0.313) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.622 (+/-0.319) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.647 (+/-0.387) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.747 (+/-0.502) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.642 (+/-0.204) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.607 (+/-0.170) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.657 (+/-0.303) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.626 (+/-0.313) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.622 (+/-0.319) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.647 (+/-0.387) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.747 (+/-0.502) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.739 (+/-0.392) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.634 (+/-0.288) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.646 (+/-0.299) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.628 (+/-0.313) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.622 (+/-0.319) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.622 (+/-0.319) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.647 (+/-0.387) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.724 (+/-0.786) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.582 (+/-0.317) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.591 (+/-0.149) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.623 (+/-0.316) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.623 (+/-0.316) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.323) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.622 (+/-0.319) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.647 (+/-0.387) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.436 (+/-0.582) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.575 (+/-0.319) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.620 (+/-0.184) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.622 (+/-0.317) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.619 (+/-0.322) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.614 (+/-0.327) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.622 (+/-0.319) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.647 (+/-0.387) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.798 (+/-0.437) for {'C': 0.1, 'kernel': 'linear'}
0.643 (+/-0.202) for {'C': 1.0, 'kernel': 'linear'}
0.650 (+/-0.295) for {'C': 10.0, 'kernel': 'linear'}
0.626 (+/-0.314) for {'C': 100.0, 'kernel': 'linear'}
0.632 (+/-0.309) for {'C': 1000.0, 'kernel': 'linear'}
0.622 (+/-0.319) for {'C': 10000.0, 'kernel': 'linear'}
0.618 (+/-0.322) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.236) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.698 (+/-0.306) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.236) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.497 (+/-0.007) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.306) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.304) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.612 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.006) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.698 (+/-0.306) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.696 (+/-0.303) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.653 (+/-0.313) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.240) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.006) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.698 (+/-0.306) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.696 (+/-0.303) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.695 (+/-0.307) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.586 (+/-0.235) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.006) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.698 (+/-0.306) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.696 (+/-0.303) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.695 (+/-0.307) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.660 (+/-0.314) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.240) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.006) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.617 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.698 (+/-0.306) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.696 (+/-0.303) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.695 (+/-0.307) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.660 (+/-0.313) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.587 (+/-0.233) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.613 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.006) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.617 (+/-0.238) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.683 (+/-0.296) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.696 (+/-0.302) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.678 (+/-0.293) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.660 (+/-0.315) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.636 (+/-0.323) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.587 (+/-0.233) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.613 (+/-0.239) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.006) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.683 (+/-0.296) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.589 (+/-0.315) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.695 (+/-0.304) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.657 (+/-0.311) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.658 (+/-0.308) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.611 (+/-0.237) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.587 (+/-0.233) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.613 (+/-0.239) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.006) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.649 (+/-0.335) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.584 (+/-0.300) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.696 (+/-0.307) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.657 (+/-0.309) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.633 (+/-0.321) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.587 (+/-0.235) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.587 (+/-0.233) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.613 (+/-0.239) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.006) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.683 (+/-0.296) for {'C': 0.1, 'kernel': 'linear'}
0.698 (+/-0.306) for {'C': 1.0, 'kernel': 'linear'}
0.696 (+/-0.304) for {'C': 10.0, 'kernel': 'linear'}
0.659 (+/-0.314) for {'C': 100.0, 'kernel': 'linear'}
0.661 (+/-0.314) for {'C': 1000.0, 'kernel': 'linear'}
0.636 (+/-0.322) for {'C': 10000.0, 'kernel': 'linear'}
0.611 (+/-0.239) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6982351183114849
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.848 (+/-0.459) for {'C': 99.99999999999997, 'kernel': 'rbf', 'gamma': 0.001}
0.715 (+/-0.319) for {'C': 99.99999999999997, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.690 (+/-0.300) for {'C': 99.99999999999997, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.690 (+/-0.300) for {'C': 99.99999999999997, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.690 (+/-0.300) for {'C': 99.99999999999997, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.656 (+/-0.218) for {'C': 99.99999999999997, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.673 (+/-0.294) for {'C': 99.99999999999997, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.673 (+/-0.294) for {'C': 99.99999999999997, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.673 (+/-0.294) for {'C': 99.99999999999997, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.681 (+/-0.297) for {'C': 99.99999999999997, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.656 (+/-0.312) for {'C': 99.99999999999997, 'kernel': 'rbf', 'gamma': 0.1}
0.715 (+/-0.319) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.001}
0.690 (+/-0.300) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.690 (+/-0.300) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.690 (+/-0.300) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.656 (+/-0.218) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.673 (+/-0.294) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.673 (+/-0.294) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.673 (+/-0.294) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.681 (+/-0.297) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.706 (+/-0.353) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.631 (+/-0.319) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.1}
0.690 (+/-0.300) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.001}
0.690 (+/-0.300) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.690 (+/-0.300) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.656 (+/-0.218) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.673 (+/-0.294) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.673 (+/-0.294) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.673 (+/-0.294) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.681 (+/-0.297) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.706 (+/-0.353) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.631 (+/-0.319) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.624 (+/-0.318) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.1}
0.690 (+/-0.300) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.001}
0.690 (+/-0.300) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.656 (+/-0.218) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.673 (+/-0.294) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.673 (+/-0.294) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.673 (+/-0.294) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.681 (+/-0.297) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.698 (+/-0.310) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.681 (+/-0.373) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.626 (+/-0.318) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.622 (+/-0.319) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.1}
0.690 (+/-0.300) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.001}
0.656 (+/-0.218) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.673 (+/-0.294) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.648 (+/-0.210) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.673 (+/-0.294) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.690 (+/-0.299) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.698 (+/-0.310) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.698 (+/-0.352) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.643 (+/-0.306) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.622 (+/-0.319) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.614 (+/-0.327) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.1}
0.656 (+/-0.218) for {'C': 999.9999999999987, 'kernel': 'rbf', 'gamma': 0.001}
0.673 (+/-0.294) for {'C': 999.9999999999987, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.648 (+/-0.210) for {'C': 999.9999999999987, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.673 (+/-0.294) for {'C': 999.9999999999987, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.681 (+/-0.297) for {'C': 999.9999999999987, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.698 (+/-0.310) for {'C': 999.9999999999987, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.698 (+/-0.352) for {'C': 999.9999999999987, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.656 (+/-0.312) for {'C': 999.9999999999987, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.622 (+/-0.319) for {'C': 999.9999999999987, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.614 (+/-0.327) for {'C': 999.9999999999987, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.614 (+/-0.327) for {'C': 999.9999999999987, 'kernel': 'rbf', 'gamma': 0.1}
0.673 (+/-0.294) for {'C': 1584.8931924611122, 'kernel': 'rbf', 'gamma': 0.001}
0.648 (+/-0.210) for {'C': 1584.8931924611122, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.673 (+/-0.294) for {'C': 1584.8931924611122, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.681 (+/-0.297) for {'C': 1584.8931924611122, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.698 (+/-0.310) for {'C': 1584.8931924611122, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.698 (+/-0.352) for {'C': 1584.8931924611122, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.656 (+/-0.312) for {'C': 1584.8931924611122, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.637 (+/-0.307) for {'C': 1584.8931924611122, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.614 (+/-0.327) for {'C': 1584.8931924611122, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.614 (+/-0.327) for {'C': 1584.8931924611122, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.620 (+/-0.320) for {'C': 1584.8931924611122, 'kernel': 'rbf', 'gamma': 0.1}
0.648 (+/-0.210) for {'C': 2511.886431509579, 'kernel': 'rbf', 'gamma': 0.001}
0.673 (+/-0.294) for {'C': 2511.886431509579, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.681 (+/-0.297) for {'C': 2511.886431509579, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.698 (+/-0.310) for {'C': 2511.886431509579, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.698 (+/-0.352) for {'C': 2511.886431509579, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.656 (+/-0.312) for {'C': 2511.886431509579, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.649 (+/-0.314) for {'C': 2511.886431509579, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.614 (+/-0.327) for {'C': 2511.886431509579, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.614 (+/-0.327) for {'C': 2511.886431509579, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.614 (+/-0.327) for {'C': 2511.886431509579, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.620 (+/-0.320) for {'C': 2511.886431509579, 'kernel': 'rbf', 'gamma': 0.1}
0.673 (+/-0.294) for {'C': 3981.0717055349724, 'kernel': 'rbf', 'gamma': 0.001}
0.681 (+/-0.297) for {'C': 3981.0717055349724, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.690 (+/-0.299) for {'C': 3981.0717055349724, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.698 (+/-0.352) for {'C': 3981.0717055349724, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.664 (+/-0.326) for {'C': 3981.0717055349724, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.644 (+/-0.309) for {'C': 3981.0717055349724, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.643 (+/-0.310) for {'C': 3981.0717055349724, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.614 (+/-0.327) for {'C': 3981.0717055349724, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.614 (+/-0.327) for {'C': 3981.0717055349724, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.620 (+/-0.320) for {'C': 3981.0717055349724, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.619 (+/-0.321) for {'C': 3981.0717055349724, 'kernel': 'rbf', 'gamma': 0.1}
0.681 (+/-0.297) for {'C': 6309.573444801927, 'kernel': 'rbf', 'gamma': 0.001}
0.690 (+/-0.299) for {'C': 6309.573444801927, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.698 (+/-0.352) for {'C': 6309.573444801927, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.664 (+/-0.326) for {'C': 6309.573444801927, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.649 (+/-0.314) for {'C': 6309.573444801927, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.643 (+/-0.310) for {'C': 6309.573444801927, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.626 (+/-0.318) for {'C': 6309.573444801927, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.614 (+/-0.327) for {'C': 6309.573444801927, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.614 (+/-0.327) for {'C': 6309.573444801927, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.620 (+/-0.320) for {'C': 6309.573444801927, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.621 (+/-0.320) for {'C': 6309.573444801927, 'kernel': 'rbf', 'gamma': 0.1}
0.690 (+/-0.299) for {'C': 9999.999999999995, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.352) for {'C': 9999.999999999995, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.664 (+/-0.326) for {'C': 9999.999999999995, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.649 (+/-0.314) for {'C': 9999.999999999995, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.646 (+/-0.317) for {'C': 9999.999999999995, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.639 (+/-0.314) for {'C': 9999.999999999995, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.626 (+/-0.318) for {'C': 9999.999999999995, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.614 (+/-0.327) for {'C': 9999.999999999995, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.614 (+/-0.327) for {'C': 9999.999999999995, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.620 (+/-0.320) for {'C': 9999.999999999995, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.622 (+/-0.319) for {'C': 9999.999999999995, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.690 (+/-0.300) for {'C': 1.0, 'kernel': 'linear'}
0.673 (+/-0.294) for {'C': 10.0, 'kernel': 'linear'}
0.648 (+/-0.315) for {'C': 100.0, 'kernel': 'linear'}
0.632 (+/-0.309) for {'C': 1000.0, 'kernel': 'linear'}
0.622 (+/-0.319) for {'C': 10000.0, 'kernel': 'linear'}
0.618 (+/-0.322) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.658 (+/-0.217) for {'C': 99.99999999999997, 'kernel': 'rbf', 'gamma': 0.001}
0.699 (+/-0.308) for {'C': 99.99999999999997, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.699 (+/-0.307) for {'C': 99.99999999999997, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.699 (+/-0.307) for {'C': 99.99999999999997, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.699 (+/-0.307) for {'C': 99.99999999999997, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.682 (+/-0.294) for {'C': 99.99999999999997, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.698 (+/-0.307) for {'C': 99.99999999999997, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.698 (+/-0.307) for {'C': 99.99999999999997, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.698 (+/-0.307) for {'C': 99.99999999999997, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.682 (+/-0.294) for {'C': 99.99999999999997, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.632 (+/-0.225) for {'C': 99.99999999999997, 'kernel': 'rbf', 'gamma': 0.1}
0.699 (+/-0.308) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.001}
0.699 (+/-0.307) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.699 (+/-0.307) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.699 (+/-0.307) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.682 (+/-0.294) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.698 (+/-0.307) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.698 (+/-0.307) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.698 (+/-0.307) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.682 (+/-0.294) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.682 (+/-0.296) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.615 (+/-0.237) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.1}
0.699 (+/-0.307) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.001}
0.699 (+/-0.307) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.699 (+/-0.307) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.682 (+/-0.294) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.698 (+/-0.307) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.698 (+/-0.307) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.698 (+/-0.307) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.682 (+/-0.294) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.682 (+/-0.296) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.615 (+/-0.237) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.614 (+/-0.237) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.1}
0.699 (+/-0.307) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.001}
0.699 (+/-0.307) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.682 (+/-0.294) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.698 (+/-0.307) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.698 (+/-0.307) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.698 (+/-0.307) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.682 (+/-0.294) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.699 (+/-0.307) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.665 (+/-0.316) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.614 (+/-0.238) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.614 (+/-0.238) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.1}
0.699 (+/-0.307) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.001}
0.682 (+/-0.294) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.698 (+/-0.307) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.698 (+/-0.307) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.698 (+/-0.307) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.699 (+/-0.307) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.699 (+/-0.308) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.682 (+/-0.296) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.640 (+/-0.235) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.614 (+/-0.237) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.587 (+/-0.233) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.1}
0.682 (+/-0.294) for {'C': 999.9999999999987, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.307) for {'C': 999.9999999999987, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.698 (+/-0.307) for {'C': 999.9999999999987, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.698 (+/-0.307) for {'C': 999.9999999999987, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.698 (+/-0.307) for {'C': 999.9999999999987, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.699 (+/-0.308) for {'C': 999.9999999999987, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.682 (+/-0.296) for {'C': 999.9999999999987, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.665 (+/-0.315) for {'C': 999.9999999999987, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.614 (+/-0.237) for {'C': 999.9999999999987, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.588 (+/-0.233) for {'C': 999.9999999999987, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.587 (+/-0.233) for {'C': 999.9999999999987, 'kernel': 'rbf', 'gamma': 0.1}
0.698 (+/-0.307) for {'C': 1584.8931924611122, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.307) for {'C': 1584.8931924611122, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.698 (+/-0.307) for {'C': 1584.8931924611122, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.698 (+/-0.307) for {'C': 1584.8931924611122, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.699 (+/-0.308) for {'C': 1584.8931924611122, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.682 (+/-0.296) for {'C': 1584.8931924611122, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.665 (+/-0.315) for {'C': 1584.8931924611122, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.639 (+/-0.235) for {'C': 1584.8931924611122, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.588 (+/-0.233) for {'C': 1584.8931924611122, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.587 (+/-0.234) for {'C': 1584.8931924611122, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.612 (+/-0.240) for {'C': 1584.8931924611122, 'kernel': 'rbf', 'gamma': 0.1}
0.698 (+/-0.307) for {'C': 2511.886431509579, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.307) for {'C': 2511.886431509579, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.698 (+/-0.307) for {'C': 2511.886431509579, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.699 (+/-0.308) for {'C': 2511.886431509579, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.682 (+/-0.296) for {'C': 2511.886431509579, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.665 (+/-0.316) for {'C': 2511.886431509579, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.664 (+/-0.316) for {'C': 2511.886431509579, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.588 (+/-0.232) for {'C': 2511.886431509579, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.587 (+/-0.234) for {'C': 2511.886431509579, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.588 (+/-0.233) for {'C': 2511.886431509579, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.612 (+/-0.240) for {'C': 2511.886431509579, 'kernel': 'rbf', 'gamma': 0.1}
0.698 (+/-0.307) for {'C': 3981.0717055349724, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.307) for {'C': 3981.0717055349724, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.698 (+/-0.307) for {'C': 3981.0717055349724, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.682 (+/-0.296) for {'C': 3981.0717055349724, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.665 (+/-0.316) for {'C': 3981.0717055349724, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.664 (+/-0.315) for {'C': 3981.0717055349724, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.663 (+/-0.316) for {'C': 3981.0717055349724, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.587 (+/-0.234) for {'C': 3981.0717055349724, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.587 (+/-0.233) for {'C': 3981.0717055349724, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.612 (+/-0.240) for {'C': 3981.0717055349724, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.612 (+/-0.240) for {'C': 3981.0717055349724, 'kernel': 'rbf', 'gamma': 0.1}
0.698 (+/-0.307) for {'C': 6309.573444801927, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.307) for {'C': 6309.573444801927, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.682 (+/-0.296) for {'C': 6309.573444801927, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.665 (+/-0.316) for {'C': 6309.573444801927, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.664 (+/-0.316) for {'C': 6309.573444801927, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.663 (+/-0.316) for {'C': 6309.573444801927, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.612 (+/-0.242) for {'C': 6309.573444801927, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.587 (+/-0.233) for {'C': 6309.573444801927, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.587 (+/-0.234) for {'C': 6309.573444801927, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.612 (+/-0.240) for {'C': 6309.573444801927, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.612 (+/-0.240) for {'C': 6309.573444801927, 'kernel': 'rbf', 'gamma': 0.1}
0.698 (+/-0.307) for {'C': 9999.999999999995, 'kernel': 'rbf', 'gamma': 0.001}
0.682 (+/-0.296) for {'C': 9999.999999999995, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.665 (+/-0.316) for {'C': 9999.999999999995, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.664 (+/-0.316) for {'C': 9999.999999999995, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.663 (+/-0.317) for {'C': 9999.999999999995, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.662 (+/-0.316) for {'C': 9999.999999999995, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.612 (+/-0.242) for {'C': 9999.999999999995, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.587 (+/-0.234) for {'C': 9999.999999999995, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.587 (+/-0.234) for {'C': 9999.999999999995, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.612 (+/-0.240) for {'C': 9999.999999999995, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.612 (+/-0.240) for {'C': 9999.999999999995, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.699 (+/-0.307) for {'C': 1.0, 'kernel': 'linear'}
0.698 (+/-0.307) for {'C': 10.0, 'kernel': 'linear'}
0.664 (+/-0.316) for {'C': 100.0, 'kernel': 'linear'}
0.661 (+/-0.314) for {'C': 1000.0, 'kernel': 'linear'}
0.636 (+/-0.322) for {'C': 10000.0, 'kernel': 'linear'}
0.611 (+/-0.239) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 99.99999999999997, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6990374309506142
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.680 (+/-0.295) for {'C': 99.99999999999997, 'kernel': 'rbf', 'gamma': 0.001}
0.665 (+/-0.225) for {'C': 99.99999999999997, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.660 (+/-0.219) for {'C': 99.99999999999997, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.660 (+/-0.219) for {'C': 99.99999999999997, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.640 (+/-0.199) for {'C': 99.99999999999997, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.605 (+/-0.165) for {'C': 99.99999999999997, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.643 (+/-0.285) for {'C': 99.99999999999997, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.646 (+/-0.284) for {'C': 99.99999999999997, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.656 (+/-0.291) for {'C': 99.99999999999997, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.650 (+/-0.296) for {'C': 99.99999999999997, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.643 (+/-0.306) for {'C': 99.99999999999997, 'kernel': 'rbf', 'gamma': 0.1}
0.665 (+/-0.225) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.001}
0.660 (+/-0.219) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.660 (+/-0.219) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.640 (+/-0.199) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.605 (+/-0.165) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.647 (+/-0.285) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.646 (+/-0.284) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.656 (+/-0.291) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.651 (+/-0.295) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.649 (+/-0.298) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.619 (+/-0.322) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.219) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.001}
0.660 (+/-0.219) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.640 (+/-0.199) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.601 (+/-0.159) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.647 (+/-0.285) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.646 (+/-0.284) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.656 (+/-0.291) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.649 (+/-0.296) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.647 (+/-0.298) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.628 (+/-0.313) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.618 (+/-0.322) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.219) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.001}
0.640 (+/-0.199) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.601 (+/-0.159) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.647 (+/-0.285) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.650 (+/-0.284) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.658 (+/-0.290) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.649 (+/-0.296) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.656 (+/-0.304) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.628 (+/-0.313) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.622 (+/-0.319) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.614 (+/-0.328) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.1}
0.640 (+/-0.199) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.001}
0.601 (+/-0.159) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.647 (+/-0.285) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.625 (+/-0.184) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.658 (+/-0.290) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.658 (+/-0.302) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.658 (+/-0.302) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.645 (+/-0.301) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.625 (+/-0.315) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.618 (+/-0.322) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.614 (+/-0.327) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.1}
0.601 (+/-0.159) for {'C': 999.9999999999987, 'kernel': 'rbf', 'gamma': 0.001}
0.647 (+/-0.285) for {'C': 999.9999999999987, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.623 (+/-0.183) for {'C': 999.9999999999987, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.658 (+/-0.290) for {'C': 999.9999999999987, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.658 (+/-0.302) for {'C': 999.9999999999987, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.658 (+/-0.302) for {'C': 999.9999999999987, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.645 (+/-0.301) for {'C': 999.9999999999987, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.626 (+/-0.314) for {'C': 999.9999999999987, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.621 (+/-0.320) for {'C': 999.9999999999987, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.619 (+/-0.322) for {'C': 999.9999999999987, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.614 (+/-0.327) for {'C': 999.9999999999987, 'kernel': 'rbf', 'gamma': 0.1}
0.647 (+/-0.285) for {'C': 1584.8931924611122, 'kernel': 'rbf', 'gamma': 0.001}
0.623 (+/-0.183) for {'C': 1584.8931924611122, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.658 (+/-0.290) for {'C': 1584.8931924611122, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.658 (+/-0.302) for {'C': 1584.8931924611122, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.658 (+/-0.302) for {'C': 1584.8931924611122, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.645 (+/-0.301) for {'C': 1584.8931924611122, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.626 (+/-0.314) for {'C': 1584.8931924611122, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.626 (+/-0.314) for {'C': 1584.8931924611122, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.623 (+/-0.319) for {'C': 1584.8931924611122, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.614 (+/-0.327) for {'C': 1584.8931924611122, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.620 (+/-0.320) for {'C': 1584.8931924611122, 'kernel': 'rbf', 'gamma': 0.1}
0.623 (+/-0.183) for {'C': 2511.886431509579, 'kernel': 'rbf', 'gamma': 0.001}
0.658 (+/-0.290) for {'C': 2511.886431509579, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.658 (+/-0.302) for {'C': 2511.886431509579, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.658 (+/-0.302) for {'C': 2511.886431509579, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.645 (+/-0.301) for {'C': 2511.886431509579, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.625 (+/-0.314) for {'C': 2511.886431509579, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.626 (+/-0.314) for {'C': 2511.886431509579, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.622 (+/-0.319) for {'C': 2511.886431509579, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.619 (+/-0.322) for {'C': 2511.886431509579, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.614 (+/-0.327) for {'C': 2511.886431509579, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.620 (+/-0.320) for {'C': 2511.886431509579, 'kernel': 'rbf', 'gamma': 0.1}
0.658 (+/-0.290) for {'C': 3981.0717055349724, 'kernel': 'rbf', 'gamma': 0.001}
0.658 (+/-0.302) for {'C': 3981.0717055349724, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.658 (+/-0.302) for {'C': 3981.0717055349724, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.645 (+/-0.301) for {'C': 3981.0717055349724, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.625 (+/-0.314) for {'C': 3981.0717055349724, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.626 (+/-0.314) for {'C': 3981.0717055349724, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.625 (+/-0.315) for {'C': 3981.0717055349724, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.623 (+/-0.319) for {'C': 3981.0717055349724, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.614 (+/-0.327) for {'C': 3981.0717055349724, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.620 (+/-0.320) for {'C': 3981.0717055349724, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.619 (+/-0.321) for {'C': 3981.0717055349724, 'kernel': 'rbf', 'gamma': 0.1}
0.658 (+/-0.302) for {'C': 6309.573444801927, 'kernel': 'rbf', 'gamma': 0.001}
0.658 (+/-0.302) for {'C': 6309.573444801927, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.646 (+/-0.301) for {'C': 6309.573444801927, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.625 (+/-0.314) for {'C': 6309.573444801927, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.626 (+/-0.314) for {'C': 6309.573444801927, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.625 (+/-0.315) for {'C': 6309.573444801927, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.623 (+/-0.319) for {'C': 6309.573444801927, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.618 (+/-0.322) for {'C': 6309.573444801927, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.614 (+/-0.327) for {'C': 6309.573444801927, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.620 (+/-0.320) for {'C': 6309.573444801927, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.621 (+/-0.320) for {'C': 6309.573444801927, 'kernel': 'rbf', 'gamma': 0.1}
0.658 (+/-0.302) for {'C': 9999.999999999995, 'kernel': 'rbf', 'gamma': 0.001}
0.646 (+/-0.301) for {'C': 9999.999999999995, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.625 (+/-0.314) for {'C': 9999.999999999995, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.626 (+/-0.314) for {'C': 9999.999999999995, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.625 (+/-0.315) for {'C': 9999.999999999995, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.627 (+/-0.313) for {'C': 9999.999999999995, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.622 (+/-0.319) for {'C': 9999.999999999995, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.614 (+/-0.327) for {'C': 9999.999999999995, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.614 (+/-0.327) for {'C': 9999.999999999995, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.620 (+/-0.320) for {'C': 9999.999999999995, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.622 (+/-0.319) for {'C': 9999.999999999995, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.798 (+/-0.437) for {'C': 0.1, 'kernel': 'linear'}
0.643 (+/-0.202) for {'C': 1.0, 'kernel': 'linear'}
0.650 (+/-0.295) for {'C': 10.0, 'kernel': 'linear'}
0.626 (+/-0.314) for {'C': 100.0, 'kernel': 'linear'}
0.632 (+/-0.309) for {'C': 1000.0, 'kernel': 'linear'}
0.622 (+/-0.319) for {'C': 10000.0, 'kernel': 'linear'}
0.618 (+/-0.322) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.698 (+/-0.306) for {'C': 99.99999999999997, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.307) for {'C': 99.99999999999997, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.698 (+/-0.306) for {'C': 99.99999999999997, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.698 (+/-0.306) for {'C': 99.99999999999997, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.698 (+/-0.305) for {'C': 99.99999999999997, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.696 (+/-0.303) for {'C': 99.99999999999997, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.697 (+/-0.304) for {'C': 99.99999999999997, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.697 (+/-0.305) for {'C': 99.99999999999997, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.697 (+/-0.305) for {'C': 99.99999999999997, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.680 (+/-0.292) for {'C': 99.99999999999997, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.653 (+/-0.313) for {'C': 99.99999999999997, 'kernel': 'rbf', 'gamma': 0.1}
0.698 (+/-0.307) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.306) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.698 (+/-0.306) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.698 (+/-0.305) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.696 (+/-0.303) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.697 (+/-0.304) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.697 (+/-0.305) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.697 (+/-0.305) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.680 (+/-0.293) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.678 (+/-0.294) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.609 (+/-0.244) for {'C': 158.48931924611128, 'kernel': 'rbf', 'gamma': 0.1}
0.698 (+/-0.306) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.306) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.698 (+/-0.305) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.696 (+/-0.303) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.697 (+/-0.304) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.697 (+/-0.305) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.697 (+/-0.305) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.679 (+/-0.292) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.678 (+/-0.294) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.659 (+/-0.315) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.609 (+/-0.243) for {'C': 251.188643150958, 'kernel': 'rbf', 'gamma': 0.1}
0.698 (+/-0.306) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.305) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.696 (+/-0.303) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.697 (+/-0.305) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.697 (+/-0.305) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.697 (+/-0.306) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.679 (+/-0.292) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.695 (+/-0.307) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.660 (+/-0.315) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.634 (+/-0.327) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.585 (+/-0.238) for {'C': 398.1071705534969, 'kernel': 'rbf', 'gamma': 0.1}
0.698 (+/-0.305) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.001}
0.696 (+/-0.303) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.697 (+/-0.305) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.697 (+/-0.305) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.697 (+/-0.306) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.696 (+/-0.305) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.695 (+/-0.307) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.676 (+/-0.296) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.659 (+/-0.313) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.609 (+/-0.243) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.585 (+/-0.237) for {'C': 630.957344480193, 'kernel': 'rbf', 'gamma': 0.1}
0.696 (+/-0.303) for {'C': 999.9999999999987, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.305) for {'C': 999.9999999999987, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.697 (+/-0.305) for {'C': 999.9999999999987, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.697 (+/-0.306) for {'C': 999.9999999999987, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.696 (+/-0.305) for {'C': 999.9999999999987, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.695 (+/-0.307) for {'C': 999.9999999999987, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.676 (+/-0.296) for {'C': 999.9999999999987, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.659 (+/-0.313) for {'C': 999.9999999999987, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.634 (+/-0.326) for {'C': 999.9999999999987, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.610 (+/-0.242) for {'C': 999.9999999999987, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.586 (+/-0.235) for {'C': 999.9999999999987, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.305) for {'C': 1584.8931924611122, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.305) for {'C': 1584.8931924611122, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.697 (+/-0.306) for {'C': 1584.8931924611122, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.696 (+/-0.305) for {'C': 1584.8931924611122, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.695 (+/-0.307) for {'C': 1584.8931924611122, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.676 (+/-0.296) for {'C': 1584.8931924611122, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.659 (+/-0.313) for {'C': 1584.8931924611122, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.659 (+/-0.313) for {'C': 1584.8931924611122, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.635 (+/-0.326) for {'C': 1584.8931924611122, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.585 (+/-0.236) for {'C': 1584.8931924611122, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.612 (+/-0.240) for {'C': 1584.8931924611122, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.305) for {'C': 2511.886431509579, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.306) for {'C': 2511.886431509579, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.696 (+/-0.305) for {'C': 2511.886431509579, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.695 (+/-0.307) for {'C': 2511.886431509579, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.676 (+/-0.296) for {'C': 2511.886431509579, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.659 (+/-0.313) for {'C': 2511.886431509579, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.659 (+/-0.314) for {'C': 2511.886431509579, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.634 (+/-0.326) for {'C': 2511.886431509579, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.610 (+/-0.242) for {'C': 2511.886431509579, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.587 (+/-0.234) for {'C': 2511.886431509579, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.612 (+/-0.240) for {'C': 2511.886431509579, 'kernel': 'rbf', 'gamma': 0.1}
0.697 (+/-0.306) for {'C': 3981.0717055349724, 'kernel': 'rbf', 'gamma': 0.001}
0.696 (+/-0.305) for {'C': 3981.0717055349724, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.695 (+/-0.307) for {'C': 3981.0717055349724, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.676 (+/-0.296) for {'C': 3981.0717055349724, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.659 (+/-0.313) for {'C': 3981.0717055349724, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.659 (+/-0.314) for {'C': 3981.0717055349724, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.659 (+/-0.313) for {'C': 3981.0717055349724, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.635 (+/-0.326) for {'C': 3981.0717055349724, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.586 (+/-0.235) for {'C': 3981.0717055349724, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.612 (+/-0.240) for {'C': 3981.0717055349724, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.612 (+/-0.240) for {'C': 3981.0717055349724, 'kernel': 'rbf', 'gamma': 0.1}
0.696 (+/-0.305) for {'C': 6309.573444801927, 'kernel': 'rbf', 'gamma': 0.001}
0.695 (+/-0.307) for {'C': 6309.573444801927, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.676 (+/-0.296) for {'C': 6309.573444801927, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.659 (+/-0.313) for {'C': 6309.573444801927, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.659 (+/-0.313) for {'C': 6309.573444801927, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.659 (+/-0.312) for {'C': 6309.573444801927, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.635 (+/-0.326) for {'C': 6309.573444801927, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.611 (+/-0.241) for {'C': 6309.573444801927, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.587 (+/-0.235) for {'C': 6309.573444801927, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.612 (+/-0.240) for {'C': 6309.573444801927, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.612 (+/-0.240) for {'C': 6309.573444801927, 'kernel': 'rbf', 'gamma': 0.1}
0.695 (+/-0.307) for {'C': 9999.999999999995, 'kernel': 'rbf', 'gamma': 0.001}
0.676 (+/-0.296) for {'C': 9999.999999999995, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
0.659 (+/-0.312) for {'C': 9999.999999999995, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.659 (+/-0.313) for {'C': 9999.999999999995, 'kernel': 'rbf', 'gamma': 0.0039810717055349725}
0.659 (+/-0.312) for {'C': 9999.999999999995, 'kernel': 'rbf', 'gamma': 0.0063095734448019355}
0.660 (+/-0.314) for {'C': 9999.999999999995, 'kernel': 'rbf', 'gamma': 0.010000000000000002}
0.636 (+/-0.324) for {'C': 9999.999999999995, 'kernel': 'rbf', 'gamma': 0.015848931924611134}
0.586 (+/-0.235) for {'C': 9999.999999999995, 'kernel': 'rbf', 'gamma': 0.02511886431509581}
0.587 (+/-0.234) for {'C': 9999.999999999995, 'kernel': 'rbf', 'gamma': 0.039810717055349734}
0.612 (+/-0.240) for {'C': 9999.999999999995, 'kernel': 'rbf', 'gamma': 0.06309573444801933}
0.612 (+/-0.240) for {'C': 9999.999999999995, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.683 (+/-0.296) for {'C': 0.1, 'kernel': 'linear'}
0.698 (+/-0.306) for {'C': 1.0, 'kernel': 'linear'}
0.696 (+/-0.304) for {'C': 10.0, 'kernel': 'linear'}
0.659 (+/-0.314) for {'C': 100.0, 'kernel': 'linear'}
0.661 (+/-0.314) for {'C': 1000.0, 'kernel': 'linear'}
0.636 (+/-0.322) for {'C': 10000.0, 'kernel': 'linear'}
0.611 (+/-0.239) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 99.99999999999997, 'kernel': 'rbf', 'gamma': 0.0015848931924611143}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6983958900156649
发现最优参数C为原先的最大/最小值,直接重新设置超参。
循环迭代之前,delta is: [9.00000000e+02 8.41510681e-03]
执行tunemodel()函数前,使用的grid search parameter是:
[{'C': array([1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02, 1.e+03, 1.e+04,
1.e+05, 1.e+06, 1.e+07]), 'kernel': ['rbf'], 'gamma': array([0.001 , 0.00109648, 0.00120226, 0.00131826, 0.00144544,
0.00158489, 0.0017378 , 0.00190546, 0.0020893 , 0.00229087,
0.00251189])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0010964781961431843}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0012022644346174128}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0013182567385564077}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0014454397707459273}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0015848931924611123}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0017378008287493752}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001905460717963248}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.002089296130854039}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.002290867652767771}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0010964781961431843}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0012022644346174128}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0013182567385564077}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0014454397707459273}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0015848931924611123}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0017378008287493752}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001905460717963248}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.002089296130854039}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.002290867652767771}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0010964781961431843}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0012022644346174128}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0013182567385564077}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0014454397707459273}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0015848931924611123}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0017378008287493752}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001905460717963248}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.002089296130854039}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.002290867652767771}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0010964781961431843}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0012022644346174128}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0013182567385564077}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0014454397707459273}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0015848931924611123}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0017378008287493752}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001905460717963248}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.002089296130854039}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.002290867652767771}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0010964781961431843}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0012022644346174128}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0013182567385564077}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0014454397707459273}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0015848931924611123}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0017378008287493752}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001905460717963248}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.002089296130854039}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.002290867652767771}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.848 (+/-0.459) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.848 (+/-0.459) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0010964781961431843}
0.823 (+/-0.451) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0012022644346174128}
0.823 (+/-0.451) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0013182567385564077}
0.748 (+/-0.389) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0014454397707459273}
0.715 (+/-0.319) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0015848931924611123}
0.698 (+/-0.319) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0017378008287493752}
0.693 (+/-0.308) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001905460717963248}
0.685 (+/-0.297) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.002089296130854039}
0.685 (+/-0.297) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.002290867652767771}
0.690 (+/-0.300) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.656 (+/-0.218) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.681 (+/-0.297) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0010964781961431843}
0.681 (+/-0.297) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0012022644346174128}
0.673 (+/-0.294) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0013182567385564077}
0.673 (+/-0.294) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0014454397707459273}
0.673 (+/-0.294) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0015848931924611123}
0.648 (+/-0.210) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0017378008287493752}
0.648 (+/-0.210) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001905460717963248}
0.648 (+/-0.210) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.002089296130854039}
0.648 (+/-0.210) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.002290867652767771}
0.648 (+/-0.210) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.690 (+/-0.299) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.310) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0010964781961431843}
0.690 (+/-0.309) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0012022644346174128}
0.698 (+/-0.352) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0013182567385564077}
0.698 (+/-0.352) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0014454397707459273}
0.698 (+/-0.352) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0015848931924611123}
0.681 (+/-0.373) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0017378008287493752}
0.681 (+/-0.373) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001905460717963248}
0.681 (+/-0.373) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.002089296130854039}
0.681 (+/-0.373) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.002290867652767771}
0.664 (+/-0.326) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.639 (+/-0.314) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.635 (+/-0.312) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0010964781961431843}
0.636 (+/-0.312) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0012022644346174128}
0.636 (+/-0.312) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0013182567385564077}
0.636 (+/-0.312) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0014454397707459273}
0.636 (+/-0.311) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0015848931924611123}
0.634 (+/-0.310) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0017378008287493752}
0.635 (+/-0.310) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001905460717963248}
0.636 (+/-0.311) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.002089296130854039}
0.636 (+/-0.311) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.002290867652767771}
0.635 (+/-0.310) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.622 (+/-0.319) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.623 (+/-0.319) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0010964781961431843}
0.622 (+/-0.319) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0012022644346174128}
0.621 (+/-0.320) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0013182567385564077}
0.624 (+/-0.318) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0014454397707459273}
0.619 (+/-0.322) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0015848931924611123}
0.620 (+/-0.321) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0017378008287493752}
0.618 (+/-0.322) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001905460717963248}
0.620 (+/-0.320) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.002089296130854039}
0.618 (+/-0.323) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.002290867652767771}
0.618 (+/-0.322) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.617 (+/-0.323) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0010964781961431843}
0.620 (+/-0.321) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0012022644346174128}
0.617 (+/-0.324) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0013182567385564077}
0.620 (+/-0.320) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0014454397707459273}
0.614 (+/-0.327) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0015848931924611123}
0.618 (+/-0.322) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0017378008287493752}
0.614 (+/-0.327) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001905460717963248}
0.614 (+/-0.327) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.002089296130854039}
0.614 (+/-0.327) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.002290867652767771}
0.614 (+/-0.327) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.690 (+/-0.300) for {'C': 1.0, 'kernel': 'linear'}
0.673 (+/-0.294) for {'C': 10.0, 'kernel': 'linear'}
0.648 (+/-0.315) for {'C': 100.0, 'kernel': 'linear'}
0.632 (+/-0.309) for {'C': 1000.0, 'kernel': 'linear'}
0.622 (+/-0.319) for {'C': 10000.0, 'kernel': 'linear'}
0.618 (+/-0.322) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0010964781961431843}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0012022644346174128}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0013182567385564077}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0014454397707459273}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0015848931924611123}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0017378008287493752}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001905460717963248}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.002089296130854039}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.002290867652767771}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0010964781961431843}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0012022644346174128}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0013182567385564077}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0014454397707459273}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0015848931924611123}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0017378008287493752}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001905460717963248}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.002089296130854039}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.002290867652767771}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0010964781961431843}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0012022644346174128}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0013182567385564077}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0014454397707459273}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0015848931924611123}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0017378008287493752}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001905460717963248}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.002089296130854039}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.002290867652767771}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0010964781961431843}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0012022644346174128}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0013182567385564077}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0014454397707459273}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0015848931924611123}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0017378008287493752}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001905460717963248}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.002089296130854039}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.002290867652767771}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0010964781961431843}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0012022644346174128}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0013182567385564077}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0014454397707459273}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0015848931924611123}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0017378008287493752}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001905460717963248}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.002089296130854039}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.002290867652767771}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.658 (+/-0.217) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.658 (+/-0.217) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0010964781961431843}
0.683 (+/-0.296) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0012022644346174128}
0.683 (+/-0.296) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0013182567385564077}
0.683 (+/-0.296) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0014454397707459273}
0.699 (+/-0.308) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0015848931924611123}
0.699 (+/-0.308) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0017378008287493752}
0.699 (+/-0.307) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001905460717963248}
0.698 (+/-0.306) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.002089296130854039}
0.698 (+/-0.306) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.002290867652767771}
0.699 (+/-0.307) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.682 (+/-0.294) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.307) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0010964781961431843}
0.698 (+/-0.307) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0012022644346174128}
0.698 (+/-0.307) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0013182567385564077}
0.698 (+/-0.307) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0014454397707459273}
0.698 (+/-0.307) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0015848931924611123}
0.698 (+/-0.307) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0017378008287493752}
0.698 (+/-0.307) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001905460717963248}
0.698 (+/-0.307) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.002089296130854039}
0.698 (+/-0.307) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.002290867652767771}
0.698 (+/-0.307) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.698 (+/-0.307) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.699 (+/-0.308) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0010964781961431843}
0.698 (+/-0.308) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0012022644346174128}
0.682 (+/-0.296) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0013182567385564077}
0.682 (+/-0.296) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0014454397707459273}
0.682 (+/-0.296) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0015848931924611123}
0.665 (+/-0.316) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0017378008287493752}
0.665 (+/-0.316) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001905460717963248}
0.665 (+/-0.317) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.002089296130854039}
0.665 (+/-0.317) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.002290867652767771}
0.665 (+/-0.316) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.662 (+/-0.316) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.662 (+/-0.316) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0010964781961431843}
0.662 (+/-0.316) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0012022644346174128}
0.662 (+/-0.316) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0013182567385564077}
0.662 (+/-0.316) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0014454397707459273}
0.662 (+/-0.316) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0015848931924611123}
0.662 (+/-0.316) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0017378008287493752}
0.662 (+/-0.316) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001905460717963248}
0.662 (+/-0.316) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.002089296130854039}
0.662 (+/-0.316) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.002290867652767771}
0.662 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.636 (+/-0.323) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.637 (+/-0.323) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0010964781961431843}
0.637 (+/-0.322) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0012022644346174128}
0.636 (+/-0.321) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0013182567385564077}
0.637 (+/-0.324) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0014454397707459273}
0.612 (+/-0.240) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0015848931924611123}
0.612 (+/-0.241) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0017378008287493752}
0.611 (+/-0.239) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001905460717963248}
0.612 (+/-0.240) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.002089296130854039}
0.611 (+/-0.238) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.002290867652767771}
0.611 (+/-0.239) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.611 (+/-0.237) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.587 (+/-0.234) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0010964781961431843}
0.636 (+/-0.318) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0012022644346174128}
0.610 (+/-0.238) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0013182567385564077}
0.612 (+/-0.240) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0014454397707459273}
0.587 (+/-0.234) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0015848931924611123}
0.612 (+/-0.239) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0017378008287493752}
0.587 (+/-0.234) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001905460717963248}
0.587 (+/-0.234) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.002089296130854039}
0.587 (+/-0.234) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.002290867652767771}
0.587 (+/-0.234) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.699 (+/-0.307) for {'C': 1.0, 'kernel': 'linear'}
0.698 (+/-0.307) for {'C': 10.0, 'kernel': 'linear'}
0.664 (+/-0.316) for {'C': 100.0, 'kernel': 'linear'}
0.661 (+/-0.314) for {'C': 1000.0, 'kernel': 'linear'}
0.636 (+/-0.322) for {'C': 10000.0, 'kernel': 'linear'}
0.611 (+/-0.239) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0015848931924611123}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6990374309506142
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0010964781961431843}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0012022644346174128}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0013182567385564077}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0014454397707459273}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0015848931924611123}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0017378008287493752}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001905460717963248}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.002089296130854039}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.002290867652767771}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0010964781961431843}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0012022644346174128}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0013182567385564077}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0014454397707459273}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0015848931924611123}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0017378008287493752}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001905460717963248}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.002089296130854039}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.002290867652767771}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0010964781961431843}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0012022644346174128}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0013182567385564077}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0014454397707459273}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0015848931924611123}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0017378008287493752}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001905460717963248}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.002089296130854039}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.002290867652767771}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0010964781961431843}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0012022644346174128}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0013182567385564077}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0014454397707459273}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0015848931924611123}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0017378008287493752}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001905460717963248}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.002089296130854039}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.002290867652767771}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0010964781961431843}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0012022644346174128}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0013182567385564077}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0014454397707459273}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0015848931924611123}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0017378008287493752}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001905460717963248}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.002089296130854039}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.002290867652767771}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.680 (+/-0.295) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.680 (+/-0.295) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0010964781961431843}
0.685 (+/-0.297) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0012022644346174128}
0.660 (+/-0.219) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0013182567385564077}
0.665 (+/-0.225) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0014454397707459273}
0.665 (+/-0.225) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0015848931924611123}
0.665 (+/-0.225) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0017378008287493752}
0.665 (+/-0.225) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001905460717963248}
0.660 (+/-0.219) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.002089296130854039}
0.660 (+/-0.219) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.002290867652767771}
0.660 (+/-0.219) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.601 (+/-0.159) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.632 (+/-0.279) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0010964781961431843}
0.636 (+/-0.280) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0012022644346174128}
0.636 (+/-0.280) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0013182567385564077}
0.643 (+/-0.285) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0014454397707459273}
0.647 (+/-0.285) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0015848931924611123}
0.625 (+/-0.184) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0017378008287493752}
0.629 (+/-0.185) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001905460717963248}
0.625 (+/-0.184) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.002089296130854039}
0.619 (+/-0.181) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.002290867652767771}
0.623 (+/-0.183) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.658 (+/-0.302) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.655 (+/-0.305) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0010964781961431843}
0.655 (+/-0.306) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0012022644346174128}
0.646 (+/-0.300) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0013182567385564077}
0.646 (+/-0.301) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0014454397707459273}
0.646 (+/-0.301) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0015848931924611123}
0.628 (+/-0.314) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0017378008287493752}
0.627 (+/-0.314) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001905460717963248}
0.626 (+/-0.314) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.002089296130854039}
0.626 (+/-0.314) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.002290867652767771}
0.625 (+/-0.314) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.626 (+/-0.313) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.626 (+/-0.313) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0010964781961431843}
0.628 (+/-0.312) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0012022644346174128}
0.628 (+/-0.312) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0013182567385564077}
0.628 (+/-0.312) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0014454397707459273}
0.629 (+/-0.311) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0015848931924611123}
0.627 (+/-0.312) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0017378008287493752}
0.628 (+/-0.312) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001905460717963248}
0.628 (+/-0.312) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.002089296130854039}
0.628 (+/-0.312) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.002290867652767771}
0.629 (+/-0.311) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.622 (+/-0.319) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.623 (+/-0.319) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0010964781961431843}
0.622 (+/-0.319) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0012022644346174128}
0.621 (+/-0.320) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0013182567385564077}
0.624 (+/-0.318) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0014454397707459273}
0.619 (+/-0.322) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0015848931924611123}
0.620 (+/-0.321) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0017378008287493752}
0.618 (+/-0.322) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001905460717963248}
0.620 (+/-0.320) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.002089296130854039}
0.618 (+/-0.323) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.002290867652767771}
0.618 (+/-0.322) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.617 (+/-0.323) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0010964781961431843}
0.620 (+/-0.321) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0012022644346174128}
0.617 (+/-0.324) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0013182567385564077}
0.620 (+/-0.320) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0014454397707459273}
0.614 (+/-0.327) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0015848931924611123}
0.618 (+/-0.322) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0017378008287493752}
0.614 (+/-0.327) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001905460717963248}
0.614 (+/-0.327) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.002089296130854039}
0.614 (+/-0.327) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.002290867652767771}
0.614 (+/-0.327) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.798 (+/-0.437) for {'C': 0.1, 'kernel': 'linear'}
0.643 (+/-0.202) for {'C': 1.0, 'kernel': 'linear'}
0.650 (+/-0.295) for {'C': 10.0, 'kernel': 'linear'}
0.626 (+/-0.314) for {'C': 100.0, 'kernel': 'linear'}
0.632 (+/-0.309) for {'C': 1000.0, 'kernel': 'linear'}
0.622 (+/-0.319) for {'C': 10000.0, 'kernel': 'linear'}
0.618 (+/-0.322) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0010964781961431843}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0012022644346174128}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0013182567385564077}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0014454397707459273}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0015848931924611123}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.0017378008287493752}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001905460717963248}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.002089296130854039}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.002290867652767771}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0010964781961431843}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0012022644346174128}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0013182567385564077}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0014454397707459273}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0015848931924611123}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0017378008287493752}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001905460717963248}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.002089296130854039}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.002290867652767771}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0010964781961431843}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0012022644346174128}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0013182567385564077}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0014454397707459273}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0015848931924611123}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0017378008287493752}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001905460717963248}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.002089296130854039}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.002290867652767771}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0010964781961431843}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0012022644346174128}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0013182567385564077}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0014454397707459273}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0015848931924611123}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0017378008287493752}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001905460717963248}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.002089296130854039}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.002290867652767771}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0010964781961431843}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0012022644346174128}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0013182567385564077}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0014454397707459273}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0015848931924611123}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0017378008287493752}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001905460717963248}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.002089296130854039}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.002290867652767771}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.698 (+/-0.306) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.306) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0010964781961431843}
0.698 (+/-0.306) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0012022644346174128}
0.698 (+/-0.306) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0013182567385564077}
0.698 (+/-0.307) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0014454397707459273}
0.698 (+/-0.307) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0015848931924611123}
0.698 (+/-0.307) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0017378008287493752}
0.698 (+/-0.307) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001905460717963248}
0.698 (+/-0.306) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.002089296130854039}
0.698 (+/-0.306) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.002290867652767771}
0.698 (+/-0.306) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.696 (+/-0.303) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.303) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0010964781961431843}
0.697 (+/-0.303) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0012022644346174128}
0.697 (+/-0.303) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0013182567385564077}
0.697 (+/-0.304) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0014454397707459273}
0.697 (+/-0.305) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0015848931924611123}
0.697 (+/-0.305) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0017378008287493752}
0.697 (+/-0.305) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001905460717963248}
0.697 (+/-0.305) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.002089296130854039}
0.697 (+/-0.305) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.002290867652767771}
0.697 (+/-0.305) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.695 (+/-0.307) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.695 (+/-0.307) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0010964781961431843}
0.694 (+/-0.307) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0012022644346174128}
0.678 (+/-0.294) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0013182567385564077}
0.677 (+/-0.295) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0014454397707459273}
0.676 (+/-0.296) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0015848931924611123}
0.659 (+/-0.315) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0017378008287493752}
0.659 (+/-0.314) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001905460717963248}
0.659 (+/-0.313) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.002089296130854039}
0.659 (+/-0.313) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.002290867652767771}
0.659 (+/-0.312) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.660 (+/-0.313) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.659 (+/-0.313) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0010964781961431843}
0.660 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0012022644346174128}
0.660 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0013182567385564077}
0.660 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0014454397707459273}
0.660 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0015848931924611123}
0.660 (+/-0.313) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0017378008287493752}
0.660 (+/-0.313) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001905460717963248}
0.660 (+/-0.314) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.002089296130854039}
0.660 (+/-0.314) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.002290867652767771}
0.661 (+/-0.313) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.636 (+/-0.323) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.637 (+/-0.323) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0010964781961431843}
0.637 (+/-0.322) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0012022644346174128}
0.636 (+/-0.321) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0013182567385564077}
0.637 (+/-0.324) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0014454397707459273}
0.612 (+/-0.240) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0015848931924611123}
0.612 (+/-0.241) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0017378008287493752}
0.611 (+/-0.239) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001905460717963248}
0.612 (+/-0.240) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.002089296130854039}
0.611 (+/-0.238) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.002290867652767771}
0.611 (+/-0.239) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.611 (+/-0.237) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.587 (+/-0.234) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0010964781961431843}
0.636 (+/-0.318) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0012022644346174128}
0.610 (+/-0.238) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0013182567385564077}
0.612 (+/-0.240) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0014454397707459273}
0.587 (+/-0.234) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0015848931924611123}
0.612 (+/-0.239) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0017378008287493752}
0.587 (+/-0.234) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001905460717963248}
0.587 (+/-0.234) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.002089296130854039}
0.587 (+/-0.234) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.002290867652767771}
0.587 (+/-0.234) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.002511886431509579}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.683 (+/-0.296) for {'C': 0.1, 'kernel': 'linear'}
0.698 (+/-0.306) for {'C': 1.0, 'kernel': 'linear'}
0.696 (+/-0.304) for {'C': 10.0, 'kernel': 'linear'}
0.659 (+/-0.314) for {'C': 100.0, 'kernel': 'linear'}
0.661 (+/-0.314) for {'C': 1000.0, 'kernel': 'linear'}
0.636 (+/-0.322) for {'C': 10000.0, 'kernel': 'linear'}
0.611 (+/-0.239) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0012022644346174128}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6983958900156649
第0轮loop中,tunemodel函数得到的最优参数是: ([{'C': array([ 10. , 15.84893192, 25.11886432, 39.81071706,
63.09573445, 100. , 158.48931925, 251.18864315,
398.10717055, 630.95734448, 1000. ]), 'kernel': ['rbf'], 'gamma': array([0.00144544, 0.00147231, 0.00149968, 0.00152757, 0.00155597,
0.00158489, 0.00161436, 0.00164437, 0.00167494, 0.00170608,
0.0017378 ])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}], {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0015848931924611123}, 0.6990374309506142)
这是第0次迭代微调C和gamma。
第0次迭代,得到delta: [2.84217094e-14 1.95156391e-18]
SVC模型clf_op_iter的参数是: {'kernel': 'rbf', 'decision_function_shape': 'ovr', 'class_weight': {-1: 15}, 'tol': 0.001, 'gamma': 0.0015848931924611123, 'random_state': 0, 'cache_size': 200, 'verbose': False, 'degree': 3, 'C': 100.0, 'probability': False, 'shrinking': True, 'coef0': 0.0, 'max_iter': -1}
训练模型SVC对训练样本的预测准确率: 0.9355067328136074
测试集中,预测为舞弊样本的有: (array([ 370, 1246, 1247, 1248, 1251, 1252, 1255, 1256], dtype=int64),)
测试集中,实际为舞弊样本的有: (array([1246, 1247, 1248, 1249, 1250, 1251, 1252, 1253, 1254, 1255, 1256],
dtype=int64),)
预测的舞弊样本数目: 8
训练模型SVC对测试样本的预测准确率: 0.9567682494684621
以上是第34次特征筛选。
第34次特征筛选,AUC值是: 0.8177805340726688
X_train_iter_svc.shape is: (1257, 18)
X_test_iter_svc.shape is: (1257, 18)
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.646 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.690 (+/-0.299) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.613 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.699 (+/-0.307) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.17 0.17 0.17 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6985566617198451
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.651 (+/-0.211) for {'C': 1.0, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.627 (+/-0.313) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.17 0.17 0.17 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.698 (+/-0.306) for {'C': 1.0, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.660 (+/-0.313) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.17 0.17 0.17 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6980753771951521
RBF的粗grid search最佳超参: {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001} RBF的粗grid search最高分值: 0.6985566617198451
linear的粗grid search最佳超参: {'C': 1.0, 'kernel': 'linear'} linear的粗grid search最高分值: 0.6980753771951521
粗grid search得到的parameter是:
[{'C': array([1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02, 1.e+03, 1.e+04, 1.e+05,
1.e+06, 1.e+07, 1.e+08]), 'kernel': ['rbf'], 'gamma': array([1.e-08, 1.e-07, 1.e-06, 1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01,
1.e+00, 1.e+01, 1.e+02])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.848 (+/-0.459) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.848 (+/-0.459) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.715 (+/-0.352) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.631 (+/-0.319) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.848 (+/-0.459) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.690 (+/-0.299) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.715 (+/-0.352) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.646 (+/-0.388) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.848 (+/-0.459) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.690 (+/-0.299) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.310) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.646 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.848 (+/-0.459) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.690 (+/-0.299) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.698 (+/-0.310) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.638 (+/-0.315) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.620 (+/-0.321) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.646 (+/-0.388) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.848 (+/-0.459) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.665 (+/-0.225) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.715 (+/-0.352) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.638 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.619 (+/-0.322) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.620 (+/-0.320) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.646 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.773 (+/-0.423) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.710 (+/-0.351) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.672 (+/-0.295) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.635 (+/-0.313) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.622 (+/-0.319) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.620 (+/-0.320) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.646 (+/-0.388) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.848 (+/-0.460) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.777 (+/-0.463) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.650 (+/-0.361) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.632 (+/-0.310) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.629 (+/-0.311) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.619 (+/-0.321) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.620 (+/-0.320) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.646 (+/-0.388) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.755 (+/-0.427) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.723 (+/-0.471) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.648 (+/-0.296) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.627 (+/-0.313) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.622 (+/-0.319) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.614 (+/-0.327) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.620 (+/-0.320) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.646 (+/-0.388) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.690 (+/-0.300) for {'C': 1.0, 'kernel': 'linear'}
0.673 (+/-0.294) for {'C': 10.0, 'kernel': 'linear'}
0.648 (+/-0.315) for {'C': 100.0, 'kernel': 'linear'}
0.627 (+/-0.313) for {'C': 1000.0, 'kernel': 'linear'}
0.622 (+/-0.319) for {'C': 10000.0, 'kernel': 'linear'}
0.621 (+/-0.320) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [1]
检查交叉验证集中测试集标签是否有预测不到的值: {-1}
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 1.00 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.658 (+/-0.217) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.002) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.658 (+/-0.217) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.699 (+/-0.307) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.236) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.007) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.658 (+/-0.217) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.699 (+/-0.307) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.699 (+/-0.308) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.612 (+/-0.241) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.009) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.658 (+/-0.217) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.699 (+/-0.307) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.699 (+/-0.307) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.587 (+/-0.234) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.009) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.658 (+/-0.217) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.699 (+/-0.307) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.699 (+/-0.308) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.662 (+/-0.316) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.241) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.009) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.658 (+/-0.217) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.698 (+/-0.307) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.699 (+/-0.308) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.662 (+/-0.316) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.610 (+/-0.242) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.009) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.699 (+/-0.309) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.699 (+/-0.306) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.698 (+/-0.306) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.661 (+/-0.316) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.635 (+/-0.324) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.587 (+/-0.234) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.239) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.009) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.683 (+/-0.296) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.679 (+/-0.282) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.696 (+/-0.302) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.662 (+/-0.313) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.659 (+/-0.316) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.611 (+/-0.241) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.587 (+/-0.234) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.239) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.009) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.699 (+/-0.306) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.646 (+/-0.247) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.696 (+/-0.302) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.661 (+/-0.312) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.634 (+/-0.328) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.586 (+/-0.235) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.587 (+/-0.234) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.239) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.009) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.699 (+/-0.307) for {'C': 1.0, 'kernel': 'linear'}
0.698 (+/-0.307) for {'C': 10.0, 'kernel': 'linear'}
0.664 (+/-0.315) for {'C': 100.0, 'kernel': 'linear'}
0.660 (+/-0.312) for {'C': 1000.0, 'kernel': 'linear'}
0.636 (+/-0.323) for {'C': 10000.0, 'kernel': 'linear'}
0.635 (+/-0.323) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6993584590650507
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.602 (+/-0.197) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.671 (+/-0.291) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.379) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.680 (+/-0.295) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.647 (+/-0.285) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.619 (+/-0.322) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.680 (+/-0.295) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.645 (+/-0.286) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.304) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.646 (+/-0.388) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.680 (+/-0.295) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.645 (+/-0.286) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.658 (+/-0.302) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.646 (+/-0.388) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.680 (+/-0.295) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.645 (+/-0.286) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.657 (+/-0.303) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.626 (+/-0.314) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.620 (+/-0.321) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.646 (+/-0.388) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.747 (+/-0.502) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.667 (+/-0.292) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.620 (+/-0.184) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.656 (+/-0.304) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.626 (+/-0.314) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.619 (+/-0.322) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.620 (+/-0.320) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.646 (+/-0.388) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.647 (+/-0.460) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.668 (+/-0.291) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.662 (+/-0.357) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.655 (+/-0.305) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.628 (+/-0.313) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.622 (+/-0.319) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.620 (+/-0.320) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.646 (+/-0.388) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.848 (+/-0.460) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.547 (+/-0.141) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.602 (+/-0.280) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.625 (+/-0.315) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.629 (+/-0.311) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.619 (+/-0.321) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.620 (+/-0.320) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.646 (+/-0.388) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.706 (+/-0.418) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.556 (+/-0.294) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.626 (+/-0.296) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.624 (+/-0.315) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.622 (+/-0.319) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.614 (+/-0.327) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.620 (+/-0.320) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.646 (+/-0.388) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.798 (+/-0.437) for {'C': 0.1, 'kernel': 'linear'}
0.651 (+/-0.211) for {'C': 1.0, 'kernel': 'linear'}
0.641 (+/-0.288) for {'C': 10.0, 'kernel': 'linear'}
0.626 (+/-0.314) for {'C': 100.0, 'kernel': 'linear'}
0.627 (+/-0.313) for {'C': 1000.0, 'kernel': 'linear'}
0.622 (+/-0.319) for {'C': 10000.0, 'kernel': 'linear'}
0.621 (+/-0.320) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.664 (+/-0.314) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.698 (+/-0.306) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.011) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.306) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.304) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.611 (+/-0.240) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.007) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.698 (+/-0.306) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.303) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.695 (+/-0.308) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.612 (+/-0.241) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.009) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.698 (+/-0.306) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.303) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.695 (+/-0.307) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.585 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.009) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.698 (+/-0.306) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.303) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.695 (+/-0.307) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.659 (+/-0.314) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.241) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.009) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.617 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.698 (+/-0.306) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.697 (+/-0.303) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.695 (+/-0.307) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.659 (+/-0.313) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.610 (+/-0.242) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.009) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.575 (+/-0.229) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.698 (+/-0.308) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.696 (+/-0.300) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.695 (+/-0.307) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.659 (+/-0.316) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.635 (+/-0.324) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.587 (+/-0.234) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.239) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.009) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.700 (+/-0.309) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.698 (+/-0.254) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.692 (+/-0.291) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.658 (+/-0.315) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.659 (+/-0.316) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.611 (+/-0.241) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.587 (+/-0.234) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.239) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.009) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.674 (+/-0.325) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.647 (+/-0.213) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.692 (+/-0.291) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.657 (+/-0.315) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.634 (+/-0.328) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.586 (+/-0.235) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.587 (+/-0.234) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.239) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.238) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.009) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.700 (+/-0.309) for {'C': 0.1, 'kernel': 'linear'}
0.698 (+/-0.306) for {'C': 1.0, 'kernel': 'linear'}
0.696 (+/-0.304) for {'C': 10.0, 'kernel': 'linear'}
0.658 (+/-0.315) for {'C': 100.0, 'kernel': 'linear'}
0.660 (+/-0.313) for {'C': 1000.0, 'kernel': 'linear'}
0.636 (+/-0.323) for {'C': 10000.0, 'kernel': 'linear'}
0.635 (+/-0.323) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.7
发现最优参数gamma为原先的最大/最小值,直接重新设置超参。
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-09}
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-08}
0.773 (+/-0.423) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-07}
0.710 (+/-0.351) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-06}
0.672 (+/-0.295) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-05}
0.635 (+/-0.313) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 0.0001}
0.622 (+/-0.319) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-09}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-08}
0.723 (+/-0.359) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-07}
0.665 (+/-0.290) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-06}
0.666 (+/-0.295) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-05}
0.635 (+/-0.312) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 0.0001}
0.622 (+/-0.319) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-09}
0.747 (+/-0.502) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-08}
0.748 (+/-0.389) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-07}
0.662 (+/-0.290) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-06}
0.655 (+/-0.295) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-05}
0.634 (+/-0.311) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 0.0001}
0.618 (+/-0.322) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-09}
0.747 (+/-0.502) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-08}
0.773 (+/-0.424) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-07}
0.675 (+/-0.358) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-06}
0.636 (+/-0.309) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-05}
0.628 (+/-0.312) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 0.0001}
0.618 (+/-0.323) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-09}
0.797 (+/-0.492) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-08}
0.773 (+/-0.417) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-07}
0.660 (+/-0.362) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-06}
0.633 (+/-0.310) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-05}
0.627 (+/-0.313) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.323) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-09}
0.848 (+/-0.460) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-08}
0.726 (+/-0.467) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-07}
0.650 (+/-0.361) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-06}
0.632 (+/-0.310) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-05}
0.629 (+/-0.311) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 0.0001}
0.619 (+/-0.321) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-09}
0.823 (+/-0.451) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-08}
0.749 (+/-0.498) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-07}
0.657 (+/-0.359) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-06}
0.630 (+/-0.311) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-05}
0.628 (+/-0.312) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 0.0001}
0.619 (+/-0.322) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-10}
0.747 (+/-0.502) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-09}
0.806 (+/-0.436) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-08}
0.749 (+/-0.499) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-07}
0.638 (+/-0.291) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-06}
0.629 (+/-0.312) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-05}
0.622 (+/-0.319) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.0001}
0.614 (+/-0.327) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-10}
0.747 (+/-0.502) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-09}
0.765 (+/-0.421) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-08}
0.748 (+/-0.499) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-07}
0.647 (+/-0.297) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-06}
0.627 (+/-0.313) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-05}
0.622 (+/-0.319) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 0.0001}
0.614 (+/-0.327) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-10}
0.797 (+/-0.492) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-09}
0.760 (+/-0.423) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-08}
0.723 (+/-0.471) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-07}
0.649 (+/-0.296) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-06}
0.628 (+/-0.313) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-05}
0.622 (+/-0.319) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 0.0001}
0.614 (+/-0.327) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-10}
0.848 (+/-0.460) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-09}
0.755 (+/-0.427) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-08}
0.723 (+/-0.471) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-07}
0.648 (+/-0.296) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-06}
0.627 (+/-0.313) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-05}
0.622 (+/-0.319) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 0.0001}
0.614 (+/-0.327) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.690 (+/-0.300) for {'C': 1.0, 'kernel': 'linear'}
0.673 (+/-0.294) for {'C': 10.0, 'kernel': 'linear'}
0.648 (+/-0.315) for {'C': 100.0, 'kernel': 'linear'}
0.627 (+/-0.313) for {'C': 1000.0, 'kernel': 'linear'}
0.622 (+/-0.319) for {'C': 10000.0, 'kernel': 'linear'}
0.621 (+/-0.320) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [1]
检查交叉验证集中测试集标签是否有预测不到的值: {-1}
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 1.00 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-09}
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-08}
0.699 (+/-0.309) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-07}
0.699 (+/-0.306) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-06}
0.698 (+/-0.306) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-05}
0.661 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 0.0001}
0.635 (+/-0.324) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-09}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-08}
0.699 (+/-0.308) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-07}
0.698 (+/-0.306) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-06}
0.697 (+/-0.306) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-05}
0.661 (+/-0.317) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 0.0001}
0.636 (+/-0.324) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-09}
0.617 (+/-0.238) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-08}
0.699 (+/-0.308) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-07}
0.698 (+/-0.305) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-06}
0.680 (+/-0.293) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-05}
0.661 (+/-0.318) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 0.0001}
0.610 (+/-0.241) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-09}
0.617 (+/-0.238) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-08}
0.699 (+/-0.309) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-07}
0.697 (+/-0.304) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-06}
0.663 (+/-0.314) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-05}
0.660 (+/-0.316) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 0.0001}
0.610 (+/-0.240) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-09}
0.642 (+/-0.236) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-08}
0.699 (+/-0.308) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-07}
0.696 (+/-0.303) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-06}
0.662 (+/-0.313) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-05}
0.659 (+/-0.315) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 0.0001}
0.610 (+/-0.240) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-09}
0.683 (+/-0.296) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-08}
0.662 (+/-0.299) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-07}
0.696 (+/-0.302) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-06}
0.662 (+/-0.313) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-05}
0.659 (+/-0.316) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 0.0001}
0.611 (+/-0.241) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-09}
0.683 (+/-0.296) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-08}
0.655 (+/-0.274) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-07}
0.696 (+/-0.302) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-06}
0.662 (+/-0.313) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-05}
0.659 (+/-0.315) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 0.0001}
0.611 (+/-0.242) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-10}
0.617 (+/-0.238) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-09}
0.700 (+/-0.308) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-08}
0.651 (+/-0.260) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-07}
0.696 (+/-0.301) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-06}
0.661 (+/-0.313) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-05}
0.634 (+/-0.327) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.0001}
0.586 (+/-0.236) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-10}
0.617 (+/-0.238) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-09}
0.699 (+/-0.307) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-08}
0.649 (+/-0.254) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-07}
0.696 (+/-0.302) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-06}
0.661 (+/-0.312) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-05}
0.634 (+/-0.327) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 0.0001}
0.586 (+/-0.235) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-10}
0.642 (+/-0.236) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-09}
0.699 (+/-0.307) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-08}
0.646 (+/-0.249) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-07}
0.696 (+/-0.302) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-06}
0.661 (+/-0.313) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-05}
0.634 (+/-0.327) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 0.0001}
0.586 (+/-0.235) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-10}
0.683 (+/-0.296) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-09}
0.699 (+/-0.306) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-08}
0.646 (+/-0.247) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-07}
0.696 (+/-0.302) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-06}
0.661 (+/-0.312) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-05}
0.634 (+/-0.328) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 0.0001}
0.586 (+/-0.235) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.699 (+/-0.307) for {'C': 1.0, 'kernel': 'linear'}
0.698 (+/-0.307) for {'C': 10.0, 'kernel': 'linear'}
0.664 (+/-0.315) for {'C': 100.0, 'kernel': 'linear'}
0.660 (+/-0.312) for {'C': 1000.0, 'kernel': 'linear'}
0.636 (+/-0.323) for {'C': 10000.0, 'kernel': 'linear'}
0.635 (+/-0.323) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-08}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6995187154753071
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-09}
0.647 (+/-0.460) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-08}
0.668 (+/-0.291) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-07}
0.662 (+/-0.357) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-06}
0.655 (+/-0.305) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-05}
0.628 (+/-0.313) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 0.0001}
0.622 (+/-0.319) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-09}
0.747 (+/-0.502) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-08}
0.669 (+/-0.296) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-07}
0.592 (+/-0.176) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-06}
0.652 (+/-0.309) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-05}
0.628 (+/-0.313) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 0.0001}
0.622 (+/-0.319) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-09}
0.747 (+/-0.502) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-08}
0.694 (+/-0.355) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-07}
0.572 (+/-0.144) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-06}
0.641 (+/-0.305) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-05}
0.628 (+/-0.312) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 0.0001}
0.618 (+/-0.322) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-09}
0.848 (+/-0.459) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-08}
0.625 (+/-0.292) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-07}
0.603 (+/-0.282) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-06}
0.624 (+/-0.316) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-05}
0.628 (+/-0.312) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 0.0001}
0.618 (+/-0.323) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-09}
0.848 (+/-0.460) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-08}
0.576 (+/-0.180) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-07}
0.599 (+/-0.280) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-06}
0.624 (+/-0.315) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-05}
0.627 (+/-0.313) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.323) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-10}
0.697 (+/-0.492) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-09}
0.848 (+/-0.460) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-08}
0.547 (+/-0.141) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-07}
0.602 (+/-0.280) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-06}
0.625 (+/-0.315) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-05}
0.629 (+/-0.311) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 0.0001}
0.619 (+/-0.321) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-10}
0.747 (+/-0.502) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-09}
0.806 (+/-0.436) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-08}
0.565 (+/-0.290) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-07}
0.610 (+/-0.282) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-06}
0.624 (+/-0.315) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-05}
0.628 (+/-0.312) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 0.0001}
0.619 (+/-0.322) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-10}
0.747 (+/-0.502) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-09}
0.748 (+/-0.447) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-08}
0.561 (+/-0.292) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-07}
0.616 (+/-0.288) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-06}
0.624 (+/-0.315) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-05}
0.622 (+/-0.319) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.0001}
0.614 (+/-0.327) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-10}
0.848 (+/-0.459) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-09}
0.710 (+/-0.415) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-08}
0.559 (+/-0.293) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-07}
0.625 (+/-0.296) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-06}
0.624 (+/-0.315) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-05}
0.622 (+/-0.319) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 0.0001}
0.614 (+/-0.327) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-10}
0.848 (+/-0.460) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-09}
0.706 (+/-0.418) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-08}
0.556 (+/-0.294) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-07}
0.626 (+/-0.296) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-06}
0.624 (+/-0.315) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-05}
0.622 (+/-0.319) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 0.0001}
0.614 (+/-0.327) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-11}
0.697 (+/-0.492) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-10}
0.848 (+/-0.460) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-09}
0.706 (+/-0.418) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-08}
0.556 (+/-0.294) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-07}
0.626 (+/-0.296) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-06}
0.624 (+/-0.315) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-05}
0.622 (+/-0.319) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 0.0001}
0.614 (+/-0.327) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.798 (+/-0.437) for {'C': 0.1, 'kernel': 'linear'}
0.651 (+/-0.211) for {'C': 1.0, 'kernel': 'linear'}
0.641 (+/-0.288) for {'C': 10.0, 'kernel': 'linear'}
0.626 (+/-0.314) for {'C': 100.0, 'kernel': 'linear'}
0.627 (+/-0.313) for {'C': 1000.0, 'kernel': 'linear'}
0.622 (+/-0.319) for {'C': 10000.0, 'kernel': 'linear'}
0.621 (+/-0.320) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-09}
0.575 (+/-0.229) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-08}
0.698 (+/-0.308) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-07}
0.696 (+/-0.300) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-06}
0.695 (+/-0.307) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-05}
0.659 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 0.0001}
0.635 (+/-0.324) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-09}
0.617 (+/-0.238) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-08}
0.698 (+/-0.309) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-07}
0.694 (+/-0.296) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-06}
0.692 (+/-0.309) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-05}
0.660 (+/-0.315) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 0.0001}
0.636 (+/-0.324) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-09}
0.617 (+/-0.238) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-08}
0.698 (+/-0.310) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-07}
0.692 (+/-0.293) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-06}
0.674 (+/-0.293) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-05}
0.660 (+/-0.315) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 0.0001}
0.610 (+/-0.241) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-09}
0.658 (+/-0.217) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-08}
0.695 (+/-0.305) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-07}
0.692 (+/-0.292) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-06}
0.658 (+/-0.313) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-05}
0.660 (+/-0.316) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 0.0001}
0.610 (+/-0.240) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-09}
0.683 (+/-0.296) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-08}
0.688 (+/-0.301) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-07}
0.691 (+/-0.290) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-06}
0.658 (+/-0.313) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-05}
0.659 (+/-0.315) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 0.0001}
0.610 (+/-0.240) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-10}
0.600 (+/-0.245) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-09}
0.700 (+/-0.309) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-08}
0.698 (+/-0.254) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-07}
0.692 (+/-0.291) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-06}
0.658 (+/-0.315) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-05}
0.659 (+/-0.316) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 0.0001}
0.611 (+/-0.241) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-10}
0.617 (+/-0.238) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-09}
0.700 (+/-0.308) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-08}
0.700 (+/-0.260) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-07}
0.692 (+/-0.290) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-06}
0.657 (+/-0.314) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-05}
0.659 (+/-0.315) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 0.0001}
0.611 (+/-0.242) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-10}
0.617 (+/-0.238) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-09}
0.675 (+/-0.325) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-08}
0.679 (+/-0.238) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-07}
0.692 (+/-0.289) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-06}
0.657 (+/-0.315) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-05}
0.634 (+/-0.327) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.0001}
0.586 (+/-0.236) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-10}
0.658 (+/-0.217) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-09}
0.674 (+/-0.325) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-08}
0.665 (+/-0.221) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-07}
0.692 (+/-0.290) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-06}
0.657 (+/-0.316) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-05}
0.634 (+/-0.327) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 0.0001}
0.586 (+/-0.235) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-10}
0.683 (+/-0.296) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-09}
0.674 (+/-0.325) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-08}
0.653 (+/-0.215) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-07}
0.692 (+/-0.290) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-06}
0.657 (+/-0.315) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-05}
0.634 (+/-0.327) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 0.0001}
0.586 (+/-0.235) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-11}
0.600 (+/-0.245) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-10}
0.700 (+/-0.309) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-09}
0.674 (+/-0.325) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-08}
0.647 (+/-0.213) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-07}
0.692 (+/-0.291) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-06}
0.657 (+/-0.315) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-05}
0.634 (+/-0.328) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 0.0001}
0.586 (+/-0.235) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.700 (+/-0.309) for {'C': 0.1, 'kernel': 'linear'}
0.698 (+/-0.306) for {'C': 1.0, 'kernel': 'linear'}
0.696 (+/-0.304) for {'C': 10.0, 'kernel': 'linear'}
0.658 (+/-0.315) for {'C': 100.0, 'kernel': 'linear'}
0.660 (+/-0.313) for {'C': 1000.0, 'kernel': 'linear'}
0.636 (+/-0.323) for {'C': 10000.0, 'kernel': 'linear'}
0.635 (+/-0.323) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-08}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.7
循环迭代之前,delta is: [5.58793545e-09 0.00000000e+00]
SVC模型clf_op_iter的参数是: {'kernel': 'rbf', 'decision_function_shape': 'ovr', 'class_weight': {-1: 15}, 'tol': 0.001, 'gamma': 1e-08, 'random_state': 0, 'cache_size': 200, 'verbose': False, 'degree': 3, 'C': 9999999.999999994, 'probability': False, 'shrinking': True, 'coef0': 0.0, 'max_iter': -1}
训练模型SVC对训练样本的预测准确率: 0.8936924167257264
测试集中,预测为舞弊样本的有: (array([1248], dtype=int64),)
测试集中,实际为舞弊样本的有: (array([1246, 1247, 1248, 1249, 1250, 1251, 1252, 1253, 1254, 1255, 1256],
dtype=int64),)
预测的舞弊样本数目: 1
训练模型SVC对测试样本的预测准确率: 0.8936924167257264
以上是第35次特征筛选。
第35次特征筛选,AUC值是: 0.5454545454545454
X_train_iter_svc.shape is: (1257, 17)
X_test_iter_svc.shape is: (1257, 17)
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.621 (+/-0.320) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.715 (+/-0.352) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.613 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.699 (+/-0.307) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.17 0.17 0.17 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6987169181301014
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.651 (+/-0.211) for {'C': 1.0, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.629 (+/-0.312) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.17 0.17 0.17 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.698 (+/-0.306) for {'C': 1.0, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.659 (+/-0.316) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.17 0.17 0.17 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6980753771951521
RBF的粗grid search最佳超参: {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001} RBF的粗grid search最高分值: 0.6987169181301014
linear的粗grid search最佳超参: {'C': 1.0, 'kernel': 'linear'} linear的粗grid search最高分值: 0.6980753771951521
粗grid search得到的parameter是:
[{'C': array([1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02, 1.e+03, 1.e+04, 1.e+05,
1.e+06, 1.e+07, 1.e+08]), 'kernel': ['rbf'], 'gamma': array([1.e-08, 1.e-07, 1.e-06, 1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01,
1.e+00, 1.e+01, 1.e+02])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.848 (+/-0.459) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.848 (+/-0.459) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.715 (+/-0.352) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.622 (+/-0.310) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.848 (+/-0.459) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.715 (+/-0.352) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.698 (+/-0.310) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.620 (+/-0.321) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.848 (+/-0.459) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.715 (+/-0.352) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.690 (+/-0.299) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.621 (+/-0.320) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.848 (+/-0.459) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.715 (+/-0.352) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.690 (+/-0.299) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.643 (+/-0.320) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.619 (+/-0.321) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.621 (+/-0.320) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.848 (+/-0.459) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.690 (+/-0.300) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.698 (+/-0.310) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.638 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.619 (+/-0.322) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.619 (+/-0.321) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.621 (+/-0.320) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.773 (+/-0.423) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.685 (+/-0.297) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.677 (+/-0.299) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.638 (+/-0.315) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.621 (+/-0.320) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.619 (+/-0.321) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.621 (+/-0.320) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.848 (+/-0.460) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.773 (+/-0.417) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.646 (+/-0.364) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.634 (+/-0.309) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.630 (+/-0.312) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.324) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.619 (+/-0.321) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.621 (+/-0.320) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.793 (+/-0.440) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.687 (+/-0.435) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.626 (+/-0.292) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.629 (+/-0.312) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.622 (+/-0.319) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.618 (+/-0.322) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.619 (+/-0.321) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.621 (+/-0.320) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.690 (+/-0.300) for {'C': 1.0, 'kernel': 'linear'}
0.659 (+/-0.287) for {'C': 10.0, 'kernel': 'linear'}
0.656 (+/-0.330) for {'C': 100.0, 'kernel': 'linear'}
0.633 (+/-0.312) for {'C': 1000.0, 'kernel': 'linear'}
0.619 (+/-0.321) for {'C': 10000.0, 'kernel': 'linear'}
0.620 (+/-0.321) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [1]
检查交叉验证集中测试集标签是否有预测不到的值: {-1}
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 1.00 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.658 (+/-0.217) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.002) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.658 (+/-0.217) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.699 (+/-0.307) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.236) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.497 (+/-0.008) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.658 (+/-0.217) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.699 (+/-0.307) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.699 (+/-0.307) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.611 (+/-0.241) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.008) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.658 (+/-0.217) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.699 (+/-0.307) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.699 (+/-0.307) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.587 (+/-0.234) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.008) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.658 (+/-0.217) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.699 (+/-0.307) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.698 (+/-0.307) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.662 (+/-0.317) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.008) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.658 (+/-0.217) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.699 (+/-0.307) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.699 (+/-0.308) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.662 (+/-0.316) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.610 (+/-0.242) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.008) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.699 (+/-0.309) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.698 (+/-0.306) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.698 (+/-0.307) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.662 (+/-0.316) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.635 (+/-0.323) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.586 (+/-0.236) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.238) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.238) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.008) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.683 (+/-0.296) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.699 (+/-0.308) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.695 (+/-0.297) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.662 (+/-0.314) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.659 (+/-0.317) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.610 (+/-0.238) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.586 (+/-0.236) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.238) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.238) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.008) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.699 (+/-0.307) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.656 (+/-0.279) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.694 (+/-0.296) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.661 (+/-0.314) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.633 (+/-0.328) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.611 (+/-0.240) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.586 (+/-0.236) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.238) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.238) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.008) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.699 (+/-0.307) for {'C': 1.0, 'kernel': 'linear'}
0.698 (+/-0.306) for {'C': 10.0, 'kernel': 'linear'}
0.664 (+/-0.316) for {'C': 100.0, 'kernel': 'linear'}
0.661 (+/-0.317) for {'C': 1000.0, 'kernel': 'linear'}
0.634 (+/-0.319) for {'C': 10000.0, 'kernel': 'linear'}
0.635 (+/-0.321) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6993584590650507
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.627 (+/-0.227) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.671 (+/-0.291) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.622 (+/-0.317) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.680 (+/-0.295) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.658 (+/-0.290) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.610 (+/-0.312) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.680 (+/-0.295) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.651 (+/-0.286) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.655 (+/-0.305) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.620 (+/-0.321) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.680 (+/-0.295) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.651 (+/-0.286) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.655 (+/-0.305) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.618 (+/-0.322) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.621 (+/-0.320) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.680 (+/-0.295) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.651 (+/-0.286) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.656 (+/-0.303) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.625 (+/-0.314) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.619 (+/-0.321) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.621 (+/-0.320) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.747 (+/-0.502) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.667 (+/-0.292) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.620 (+/-0.186) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.657 (+/-0.303) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.625 (+/-0.314) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.618 (+/-0.323) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.619 (+/-0.321) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.621 (+/-0.320) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.647 (+/-0.460) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.668 (+/-0.291) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.623 (+/-0.289) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.655 (+/-0.304) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.626 (+/-0.314) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.620 (+/-0.321) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.619 (+/-0.321) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.621 (+/-0.320) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.848 (+/-0.460) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.543 (+/-0.142) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.603 (+/-0.280) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.624 (+/-0.315) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.626 (+/-0.314) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.324) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.619 (+/-0.321) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.621 (+/-0.320) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.793 (+/-0.440) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.557 (+/-0.294) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.610 (+/-0.283) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.624 (+/-0.315) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.621 (+/-0.320) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.618 (+/-0.322) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.619 (+/-0.321) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.621 (+/-0.320) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.798 (+/-0.437) for {'C': 0.1, 'kernel': 'linear'}
0.651 (+/-0.211) for {'C': 1.0, 'kernel': 'linear'}
0.632 (+/-0.282) for {'C': 10.0, 'kernel': 'linear'}
0.626 (+/-0.314) for {'C': 100.0, 'kernel': 'linear'}
0.629 (+/-0.312) for {'C': 1000.0, 'kernel': 'linear'}
0.619 (+/-0.321) for {'C': 10000.0, 'kernel': 'linear'}
0.620 (+/-0.321) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.665 (+/-0.314) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.698 (+/-0.306) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.495 (+/-0.011) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.306) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.698 (+/-0.305) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.611 (+/-0.240) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.497 (+/-0.008) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.698 (+/-0.306) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.304) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.695 (+/-0.307) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.611 (+/-0.241) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.008) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.698 (+/-0.306) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.304) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.695 (+/-0.307) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.609 (+/-0.243) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.008) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.698 (+/-0.306) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.304) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.695 (+/-0.307) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.658 (+/-0.313) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.008) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.617 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.698 (+/-0.306) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.697 (+/-0.304) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.695 (+/-0.307) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.658 (+/-0.313) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.610 (+/-0.241) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.008) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.575 (+/-0.229) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.698 (+/-0.308) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.695 (+/-0.299) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.695 (+/-0.307) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.658 (+/-0.315) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.634 (+/-0.322) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.586 (+/-0.236) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.238) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.238) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.008) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.700 (+/-0.309) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.672 (+/-0.283) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.691 (+/-0.287) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.657 (+/-0.316) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.658 (+/-0.315) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.610 (+/-0.238) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.586 (+/-0.236) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.238) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.238) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.008) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.699 (+/-0.307) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.648 (+/-0.236) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.691 (+/-0.288) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.657 (+/-0.316) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.633 (+/-0.327) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.611 (+/-0.240) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.586 (+/-0.236) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.238) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.238) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.008) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.700 (+/-0.309) for {'C': 0.1, 'kernel': 'linear'}
0.698 (+/-0.306) for {'C': 1.0, 'kernel': 'linear'}
0.696 (+/-0.303) for {'C': 10.0, 'kernel': 'linear'}
0.658 (+/-0.315) for {'C': 100.0, 'kernel': 'linear'}
0.659 (+/-0.316) for {'C': 1000.0, 'kernel': 'linear'}
0.634 (+/-0.319) for {'C': 10000.0, 'kernel': 'linear'}
0.635 (+/-0.321) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.7
发现最优参数gamma为原先的最大/最小值,直接重新设置超参。
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-09}
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-08}
0.773 (+/-0.423) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-07}
0.685 (+/-0.297) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-06}
0.677 (+/-0.299) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-05}
0.638 (+/-0.315) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 0.0001}
0.627 (+/-0.312) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-09}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-08}
0.748 (+/-0.389) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-07}
0.687 (+/-0.351) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-06}
0.672 (+/-0.295) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-05}
0.633 (+/-0.312) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 0.0001}
0.619 (+/-0.321) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-09}
0.747 (+/-0.502) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-08}
0.748 (+/-0.396) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-07}
0.680 (+/-0.350) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-06}
0.656 (+/-0.293) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-05}
0.634 (+/-0.311) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 0.0001}
0.620 (+/-0.321) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-09}
0.747 (+/-0.502) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-08}
0.740 (+/-0.392) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-07}
0.638 (+/-0.287) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-06}
0.636 (+/-0.309) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-05}
0.633 (+/-0.312) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 0.0001}
0.619 (+/-0.321) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-09}
0.797 (+/-0.492) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-08}
0.760 (+/-0.427) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-07}
0.652 (+/-0.365) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-06}
0.636 (+/-0.309) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-05}
0.630 (+/-0.312) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.324) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-09}
0.848 (+/-0.460) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-08}
0.773 (+/-0.417) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-07}
0.646 (+/-0.364) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-06}
0.634 (+/-0.309) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-05}
0.630 (+/-0.312) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.324) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-09}
0.823 (+/-0.451) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-08}
0.787 (+/-0.446) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-07}
0.652 (+/-0.363) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-06}
0.631 (+/-0.311) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-05}
0.625 (+/-0.318) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.323) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-10}
0.747 (+/-0.502) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-09}
0.806 (+/-0.436) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-08}
0.703 (+/-0.429) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-07}
0.650 (+/-0.364) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-06}
0.631 (+/-0.311) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-05}
0.625 (+/-0.318) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.0001}
0.618 (+/-0.322) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-10}
0.747 (+/-0.502) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-09}
0.798 (+/-0.437) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-08}
0.712 (+/-0.472) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-07}
0.652 (+/-0.363) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-06}
0.629 (+/-0.312) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-05}
0.625 (+/-0.318) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 0.0001}
0.618 (+/-0.322) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-10}
0.797 (+/-0.492) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-09}
0.793 (+/-0.440) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-08}
0.687 (+/-0.434) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-07}
0.627 (+/-0.291) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-06}
0.629 (+/-0.312) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-05}
0.624 (+/-0.318) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 0.0001}
0.618 (+/-0.322) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-10}
0.848 (+/-0.460) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-09}
0.793 (+/-0.440) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-08}
0.687 (+/-0.435) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-07}
0.626 (+/-0.292) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-06}
0.629 (+/-0.312) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-05}
0.622 (+/-0.319) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 0.0001}
0.618 (+/-0.322) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.690 (+/-0.300) for {'C': 1.0, 'kernel': 'linear'}
0.659 (+/-0.287) for {'C': 10.0, 'kernel': 'linear'}
0.656 (+/-0.330) for {'C': 100.0, 'kernel': 'linear'}
0.633 (+/-0.312) for {'C': 1000.0, 'kernel': 'linear'}
0.619 (+/-0.321) for {'C': 10000.0, 'kernel': 'linear'}
0.620 (+/-0.321) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [1]
检查交叉验证集中测试集标签是否有预测不到的值: {-1}
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 1.00 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-09}
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-08}
0.699 (+/-0.309) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-07}
0.698 (+/-0.306) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-06}
0.698 (+/-0.307) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-05}
0.662 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 0.0001}
0.660 (+/-0.313) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-09}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-08}
0.699 (+/-0.308) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-07}
0.698 (+/-0.305) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-06}
0.698 (+/-0.307) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-05}
0.661 (+/-0.315) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 0.0001}
0.634 (+/-0.320) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-09}
0.617 (+/-0.238) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-08}
0.699 (+/-0.309) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-07}
0.698 (+/-0.304) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-06}
0.680 (+/-0.294) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-05}
0.661 (+/-0.317) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 0.0001}
0.634 (+/-0.320) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-09}
0.617 (+/-0.238) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-08}
0.699 (+/-0.308) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-07}
0.696 (+/-0.301) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-06}
0.663 (+/-0.313) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-05}
0.660 (+/-0.317) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 0.0001}
0.634 (+/-0.319) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-09}
0.642 (+/-0.236) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-08}
0.699 (+/-0.308) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-07}
0.695 (+/-0.299) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-06}
0.663 (+/-0.314) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-05}
0.659 (+/-0.317) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 0.0001}
0.610 (+/-0.239) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-09}
0.683 (+/-0.296) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-08}
0.699 (+/-0.308) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-07}
0.695 (+/-0.297) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-06}
0.662 (+/-0.314) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-05}
0.659 (+/-0.317) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 0.0001}
0.610 (+/-0.238) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-09}
0.683 (+/-0.296) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-08}
0.682 (+/-0.294) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-07}
0.695 (+/-0.297) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-06}
0.662 (+/-0.314) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-05}
0.634 (+/-0.329) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 0.0001}
0.611 (+/-0.239) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-10}
0.617 (+/-0.238) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-09}
0.700 (+/-0.308) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-08}
0.663 (+/-0.305) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-07}
0.695 (+/-0.298) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-06}
0.661 (+/-0.315) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-05}
0.634 (+/-0.330) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.0001}
0.611 (+/-0.240) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-10}
0.617 (+/-0.238) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-09}
0.699 (+/-0.307) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-08}
0.659 (+/-0.290) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-07}
0.695 (+/-0.298) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-06}
0.661 (+/-0.314) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-05}
0.634 (+/-0.330) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 0.0001}
0.611 (+/-0.239) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-10}
0.642 (+/-0.236) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-09}
0.699 (+/-0.307) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-08}
0.657 (+/-0.283) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-07}
0.694 (+/-0.298) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-06}
0.661 (+/-0.314) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-05}
0.634 (+/-0.330) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 0.0001}
0.611 (+/-0.239) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-10}
0.683 (+/-0.296) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-09}
0.699 (+/-0.307) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-08}
0.656 (+/-0.279) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-07}
0.694 (+/-0.296) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-06}
0.661 (+/-0.314) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-05}
0.633 (+/-0.328) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 0.0001}
0.611 (+/-0.240) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.699 (+/-0.307) for {'C': 1.0, 'kernel': 'linear'}
0.698 (+/-0.306) for {'C': 10.0, 'kernel': 'linear'}
0.664 (+/-0.316) for {'C': 100.0, 'kernel': 'linear'}
0.661 (+/-0.317) for {'C': 1000.0, 'kernel': 'linear'}
0.634 (+/-0.319) for {'C': 10000.0, 'kernel': 'linear'}
0.635 (+/-0.321) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-08}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6995187154753071
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-09}
0.647 (+/-0.460) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-08}
0.668 (+/-0.291) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-07}
0.623 (+/-0.289) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-06}
0.655 (+/-0.304) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-05}
0.626 (+/-0.314) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 0.0001}
0.626 (+/-0.313) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-09}
0.747 (+/-0.502) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-08}
0.669 (+/-0.296) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-07}
0.636 (+/-0.369) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-06}
0.652 (+/-0.308) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-05}
0.626 (+/-0.314) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 0.0001}
0.619 (+/-0.321) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-09}
0.747 (+/-0.502) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-08}
0.684 (+/-0.362) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-07}
0.598 (+/-0.284) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-06}
0.641 (+/-0.304) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-05}
0.627 (+/-0.313) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 0.0001}
0.620 (+/-0.321) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-09}
0.848 (+/-0.459) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-08}
0.610 (+/-0.220) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-07}
0.599 (+/-0.284) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-06}
0.624 (+/-0.316) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-05}
0.626 (+/-0.313) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 0.0001}
0.619 (+/-0.321) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-09}
0.848 (+/-0.460) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-08}
0.564 (+/-0.146) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-07}
0.599 (+/-0.280) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-06}
0.624 (+/-0.315) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-05}
0.627 (+/-0.313) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.324) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-10}
0.697 (+/-0.492) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-09}
0.848 (+/-0.460) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-08}
0.543 (+/-0.142) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-07}
0.603 (+/-0.280) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-06}
0.624 (+/-0.315) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-05}
0.626 (+/-0.314) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.324) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-10}
0.747 (+/-0.502) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-09}
0.806 (+/-0.436) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-08}
0.538 (+/-0.143) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-07}
0.610 (+/-0.283) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-06}
0.624 (+/-0.315) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-05}
0.622 (+/-0.319) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.323) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-10}
0.747 (+/-0.502) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-09}
0.798 (+/-0.437) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-08}
0.534 (+/-0.144) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-07}
0.607 (+/-0.283) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-06}
0.624 (+/-0.315) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-05}
0.621 (+/-0.320) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.0001}
0.618 (+/-0.322) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-10}
0.848 (+/-0.459) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-09}
0.798 (+/-0.437) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-08}
0.557 (+/-0.294) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-07}
0.609 (+/-0.283) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-06}
0.624 (+/-0.315) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-05}
0.621 (+/-0.320) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 0.0001}
0.618 (+/-0.322) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-10}
0.848 (+/-0.460) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-09}
0.793 (+/-0.440) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-08}
0.557 (+/-0.294) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-07}
0.609 (+/-0.283) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-06}
0.624 (+/-0.315) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-05}
0.621 (+/-0.320) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 0.0001}
0.618 (+/-0.322) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-11}
0.697 (+/-0.492) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-10}
0.848 (+/-0.460) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-09}
0.793 (+/-0.440) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-08}
0.557 (+/-0.294) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-07}
0.610 (+/-0.283) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-06}
0.624 (+/-0.315) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-05}
0.621 (+/-0.320) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 0.0001}
0.618 (+/-0.322) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.798 (+/-0.437) for {'C': 0.1, 'kernel': 'linear'}
0.651 (+/-0.211) for {'C': 1.0, 'kernel': 'linear'}
0.632 (+/-0.282) for {'C': 10.0, 'kernel': 'linear'}
0.626 (+/-0.314) for {'C': 100.0, 'kernel': 'linear'}
0.629 (+/-0.312) for {'C': 1000.0, 'kernel': 'linear'}
0.619 (+/-0.321) for {'C': 10000.0, 'kernel': 'linear'}
0.620 (+/-0.321) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-09}
0.575 (+/-0.229) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-08}
0.698 (+/-0.308) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-07}
0.695 (+/-0.299) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-06}
0.695 (+/-0.307) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-05}
0.658 (+/-0.315) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 0.0001}
0.659 (+/-0.311) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-09}
0.617 (+/-0.238) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-08}
0.698 (+/-0.309) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-07}
0.693 (+/-0.293) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-06}
0.693 (+/-0.308) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-05}
0.659 (+/-0.315) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 0.0001}
0.634 (+/-0.320) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-09}
0.617 (+/-0.238) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-08}
0.681 (+/-0.296) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-07}
0.691 (+/-0.290) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-06}
0.675 (+/-0.294) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-05}
0.659 (+/-0.314) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 0.0001}
0.634 (+/-0.320) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-09}
0.658 (+/-0.217) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-08}
0.694 (+/-0.307) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-07}
0.690 (+/-0.287) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-06}
0.658 (+/-0.313) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-05}
0.659 (+/-0.315) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 0.0001}
0.634 (+/-0.319) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-09}
0.683 (+/-0.296) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-08}
0.687 (+/-0.298) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-07}
0.691 (+/-0.290) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-06}
0.657 (+/-0.315) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-05}
0.658 (+/-0.317) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 0.0001}
0.610 (+/-0.239) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-10}
0.600 (+/-0.245) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-09}
0.700 (+/-0.309) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-08}
0.672 (+/-0.284) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-07}
0.691 (+/-0.287) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-06}
0.657 (+/-0.316) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-05}
0.658 (+/-0.315) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 0.0001}
0.610 (+/-0.238) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-10}
0.617 (+/-0.238) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-09}
0.700 (+/-0.308) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-08}
0.669 (+/-0.247) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-07}
0.691 (+/-0.287) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-06}
0.657 (+/-0.316) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-05}
0.633 (+/-0.327) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 0.0001}
0.611 (+/-0.239) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-10}
0.617 (+/-0.238) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-09}
0.699 (+/-0.307) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-08}
0.671 (+/-0.269) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-07}
0.691 (+/-0.288) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-06}
0.657 (+/-0.315) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-05}
0.633 (+/-0.326) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.0001}
0.611 (+/-0.240) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-10}
0.658 (+/-0.217) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-09}
0.699 (+/-0.307) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-08}
0.660 (+/-0.251) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-07}
0.691 (+/-0.288) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-06}
0.657 (+/-0.315) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-05}
0.633 (+/-0.326) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 0.0001}
0.611 (+/-0.239) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-10}
0.683 (+/-0.296) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-09}
0.699 (+/-0.307) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-08}
0.653 (+/-0.246) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-07}
0.691 (+/-0.288) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-06}
0.657 (+/-0.315) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-05}
0.633 (+/-0.326) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 0.0001}
0.611 (+/-0.239) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-11}
0.600 (+/-0.245) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-10}
0.700 (+/-0.309) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-09}
0.699 (+/-0.307) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-08}
0.648 (+/-0.236) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-07}
0.691 (+/-0.288) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-06}
0.657 (+/-0.316) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-05}
0.633 (+/-0.327) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 0.0001}
0.611 (+/-0.240) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.700 (+/-0.309) for {'C': 0.1, 'kernel': 'linear'}
0.698 (+/-0.306) for {'C': 1.0, 'kernel': 'linear'}
0.696 (+/-0.303) for {'C': 10.0, 'kernel': 'linear'}
0.658 (+/-0.315) for {'C': 100.0, 'kernel': 'linear'}
0.659 (+/-0.316) for {'C': 1000.0, 'kernel': 'linear'}
0.634 (+/-0.319) for {'C': 10000.0, 'kernel': 'linear'}
0.635 (+/-0.321) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-08}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.7
循环迭代之前,delta is: [5.58793545e-09 0.00000000e+00]
SVC模型clf_op_iter的参数是: {'kernel': 'rbf', 'decision_function_shape': 'ovr', 'class_weight': {-1: 15}, 'tol': 0.001, 'gamma': 1e-08, 'random_state': 0, 'cache_size': 200, 'verbose': False, 'degree': 3, 'C': 9999999.999999994, 'probability': False, 'shrinking': True, 'coef0': 0.0, 'max_iter': -1}
训练模型SVC对训练样本的预测准确率: 0.8936924167257264
测试集中,预测为舞弊样本的有: (array([1248], dtype=int64),)
测试集中,实际为舞弊样本的有: (array([1246, 1247, 1248, 1249, 1250, 1251, 1252, 1253, 1254, 1255, 1256],
dtype=int64),)
预测的舞弊样本数目: 1
训练模型SVC对测试样本的预测准确率: 0.8936924167257264
以上是第36次特征筛选。
第36次特征筛选,AUC值是: 0.5454545454545454
X_train_iter_svc.shape is: (1257, 16)
X_test_iter_svc.shape is: (1257, 16)
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.722 (+/-0.473) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.621 (+/-0.320) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.665 (+/-0.225) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.613 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.698 (+/-0.307) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.17 0.17 0.17 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6983958900156649
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.643 (+/-0.202) for {'C': 1.0, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.628 (+/-0.313) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.17 0.17 0.17 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.698 (+/-0.306) for {'C': 1.0, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.660 (+/-0.315) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.17 0.17 0.17 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6979151207848958
RBF的粗grid search最佳超参: {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001} RBF的粗grid search最高分值: 0.6983958900156649
linear的粗grid search最佳超参: {'C': 1.0, 'kernel': 'linear'} linear的粗grid search最高分值: 0.6979151207848958
粗grid search得到的parameter是:
[{'C': array([1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02, 1.e+03, 1.e+04, 1.e+05,
1.e+06, 1.e+07, 1.e+08]), 'kernel': ['rbf'], 'gamma': array([1.e-08, 1.e-07, 1.e-06, 1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01,
1.e+00, 1.e+01, 1.e+02])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.848 (+/-0.459) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.848 (+/-0.459) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.690 (+/-0.300) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.622 (+/-0.310) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.848 (+/-0.459) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.690 (+/-0.300) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.698 (+/-0.310) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.620 (+/-0.321) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.848 (+/-0.459) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.665 (+/-0.225) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.690 (+/-0.299) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.614 (+/-0.327) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.621 (+/-0.320) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.848 (+/-0.459) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.665 (+/-0.225) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.690 (+/-0.299) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.635 (+/-0.313) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.619 (+/-0.321) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.621 (+/-0.320) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.848 (+/-0.459) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.665 (+/-0.225) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.690 (+/-0.299) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.643 (+/-0.320) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.618 (+/-0.322) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.619 (+/-0.321) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.621 (+/-0.320) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.748 (+/-0.396) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.723 (+/-0.359) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.669 (+/-0.295) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.638 (+/-0.315) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.625 (+/-0.314) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.619 (+/-0.321) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.621 (+/-0.320) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.848 (+/-0.460) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.781 (+/-0.418) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.655 (+/-0.362) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.634 (+/-0.311) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.630 (+/-0.311) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.619 (+/-0.322) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.619 (+/-0.321) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.621 (+/-0.320) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.793 (+/-0.440) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.687 (+/-0.434) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.613 (+/-0.310) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.628 (+/-0.313) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.624 (+/-0.318) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.620 (+/-0.320) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.619 (+/-0.321) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.621 (+/-0.320) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.690 (+/-0.300) for {'C': 1.0, 'kernel': 'linear'}
0.659 (+/-0.287) for {'C': 10.0, 'kernel': 'linear'}
0.648 (+/-0.315) for {'C': 100.0, 'kernel': 'linear'}
0.631 (+/-0.312) for {'C': 1000.0, 'kernel': 'linear'}
0.619 (+/-0.321) for {'C': 10000.0, 'kernel': 'linear'}
0.620 (+/-0.321) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [1]
检查交叉验证集中测试集标签是否有预测不到的值: {-1}
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 1.00 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.658 (+/-0.217) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.002) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.658 (+/-0.217) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.699 (+/-0.307) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.236) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.497 (+/-0.008) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.658 (+/-0.217) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.699 (+/-0.307) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.699 (+/-0.307) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.612 (+/-0.241) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.008) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.658 (+/-0.217) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.698 (+/-0.307) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.699 (+/-0.307) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.587 (+/-0.234) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.008) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.658 (+/-0.217) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.698 (+/-0.307) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.698 (+/-0.307) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.662 (+/-0.315) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.008) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.658 (+/-0.217) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.698 (+/-0.307) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.698 (+/-0.307) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.662 (+/-0.317) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.610 (+/-0.241) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.008) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.699 (+/-0.308) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.699 (+/-0.307) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.698 (+/-0.306) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.662 (+/-0.316) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.659 (+/-0.313) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.586 (+/-0.236) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.238) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.238) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.008) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.683 (+/-0.296) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.699 (+/-0.309) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.696 (+/-0.299) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.662 (+/-0.314) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.660 (+/-0.317) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.634 (+/-0.317) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.585 (+/-0.237) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.238) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.238) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.008) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.699 (+/-0.307) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.657 (+/-0.283) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.638 (+/-0.318) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.660 (+/-0.314) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.634 (+/-0.328) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.636 (+/-0.320) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.585 (+/-0.237) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.238) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.238) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.008) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.699 (+/-0.307) for {'C': 1.0, 'kernel': 'linear'}
0.698 (+/-0.306) for {'C': 10.0, 'kernel': 'linear'}
0.664 (+/-0.315) for {'C': 100.0, 'kernel': 'linear'}
0.661 (+/-0.316) for {'C': 1000.0, 'kernel': 'linear'}
0.634 (+/-0.319) for {'C': 10000.0, 'kernel': 'linear'}
0.635 (+/-0.321) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6991982026547944
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.624 (+/-0.227) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.671 (+/-0.291) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.622 (+/-0.317) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.671 (+/-0.291) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.633 (+/-0.197) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.610 (+/-0.312) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.671 (+/-0.291) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.622 (+/-0.186) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.655 (+/-0.305) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.620 (+/-0.321) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.671 (+/-0.291) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.622 (+/-0.186) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.655 (+/-0.305) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.323) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.621 (+/-0.320) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.671 (+/-0.291) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.622 (+/-0.186) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.655 (+/-0.304) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.625 (+/-0.314) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.619 (+/-0.321) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.621 (+/-0.320) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.747 (+/-0.502) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.680 (+/-0.295) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.620 (+/-0.186) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.656 (+/-0.303) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.625 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.618 (+/-0.323) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.619 (+/-0.321) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.621 (+/-0.320) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.647 (+/-0.460) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.668 (+/-0.291) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.613 (+/-0.189) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.656 (+/-0.303) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.626 (+/-0.314) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.625 (+/-0.315) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.619 (+/-0.321) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.621 (+/-0.320) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.848 (+/-0.460) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.543 (+/-0.142) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.615 (+/-0.283) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.625 (+/-0.315) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.627 (+/-0.313) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.619 (+/-0.322) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.619 (+/-0.321) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.621 (+/-0.320) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.793 (+/-0.440) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.557 (+/-0.294) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.595 (+/-0.298) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.625 (+/-0.315) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.623 (+/-0.319) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.620 (+/-0.320) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.327) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.619 (+/-0.321) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.621 (+/-0.320) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.798 (+/-0.437) for {'C': 0.1, 'kernel': 'linear'}
0.643 (+/-0.202) for {'C': 1.0, 'kernel': 'linear'}
0.632 (+/-0.282) for {'C': 10.0, 'kernel': 'linear'}
0.625 (+/-0.314) for {'C': 100.0, 'kernel': 'linear'}
0.628 (+/-0.313) for {'C': 1000.0, 'kernel': 'linear'}
0.619 (+/-0.321) for {'C': 10000.0, 'kernel': 'linear'}
0.620 (+/-0.321) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.665 (+/-0.314) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.698 (+/-0.306) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.011) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.306) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.305) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.611 (+/-0.240) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.497 (+/-0.008) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.698 (+/-0.306) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.304) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.695 (+/-0.307) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.612 (+/-0.241) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.008) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.698 (+/-0.306) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.304) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.695 (+/-0.307) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.609 (+/-0.243) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.008) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.698 (+/-0.306) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.304) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.695 (+/-0.307) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.658 (+/-0.314) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.008) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.617 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.698 (+/-0.306) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.697 (+/-0.304) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.695 (+/-0.307) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.658 (+/-0.313) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.610 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.008) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.575 (+/-0.229) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.698 (+/-0.308) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.696 (+/-0.302) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.695 (+/-0.307) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.659 (+/-0.315) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.658 (+/-0.312) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.586 (+/-0.236) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.238) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.238) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.008) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.700 (+/-0.309) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.672 (+/-0.283) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.694 (+/-0.296) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.657 (+/-0.316) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.659 (+/-0.315) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.634 (+/-0.317) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.585 (+/-0.237) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.238) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.238) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.008) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.699 (+/-0.307) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.648 (+/-0.236) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.636 (+/-0.312) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.657 (+/-0.317) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.634 (+/-0.328) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.636 (+/-0.320) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.585 (+/-0.237) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.238) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.238) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.008) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.700 (+/-0.309) for {'C': 0.1, 'kernel': 'linear'}
0.698 (+/-0.306) for {'C': 1.0, 'kernel': 'linear'}
0.696 (+/-0.303) for {'C': 10.0, 'kernel': 'linear'}
0.658 (+/-0.314) for {'C': 100.0, 'kernel': 'linear'}
0.660 (+/-0.315) for {'C': 1000.0, 'kernel': 'linear'}
0.634 (+/-0.319) for {'C': 10000.0, 'kernel': 'linear'}
0.635 (+/-0.321) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.7
发现最优参数gamma为原先的最大/最小值,直接重新设置超参。
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-09}
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-08}
0.748 (+/-0.396) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-07}
0.723 (+/-0.359) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-06}
0.669 (+/-0.295) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-05}
0.638 (+/-0.315) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 0.0001}
0.625 (+/-0.314) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-09}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-08}
0.748 (+/-0.389) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-07}
0.715 (+/-0.352) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-06}
0.669 (+/-0.295) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-05}
0.636 (+/-0.311) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 0.0001}
0.618 (+/-0.322) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-09}
0.747 (+/-0.502) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-08}
0.748 (+/-0.396) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-07}
0.710 (+/-0.351) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-06}
0.666 (+/-0.297) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-05}
0.632 (+/-0.310) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 0.0001}
0.619 (+/-0.322) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-09}
0.747 (+/-0.502) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-08}
0.740 (+/-0.392) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-07}
0.675 (+/-0.360) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-06}
0.637 (+/-0.308) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-05}
0.632 (+/-0.311) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 0.0001}
0.618 (+/-0.322) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-09}
0.797 (+/-0.492) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-08}
0.760 (+/-0.427) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-07}
0.657 (+/-0.360) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-06}
0.635 (+/-0.310) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-05}
0.631 (+/-0.311) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 0.0001}
0.618 (+/-0.322) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-09}
0.848 (+/-0.460) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-08}
0.781 (+/-0.418) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-07}
0.655 (+/-0.362) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-06}
0.634 (+/-0.311) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-05}
0.630 (+/-0.311) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 0.0001}
0.619 (+/-0.322) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-09}
0.823 (+/-0.451) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-08}
0.787 (+/-0.446) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-07}
0.660 (+/-0.359) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-06}
0.630 (+/-0.312) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-05}
0.623 (+/-0.319) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 0.0001}
0.620 (+/-0.321) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-10}
0.747 (+/-0.502) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-09}
0.806 (+/-0.436) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-08}
0.705 (+/-0.426) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-07}
0.643 (+/-0.372) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-06}
0.630 (+/-0.312) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-05}
0.623 (+/-0.319) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.0001}
0.620 (+/-0.321) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-10}
0.747 (+/-0.502) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-09}
0.798 (+/-0.437) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-08}
0.713 (+/-0.471) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-07}
0.651 (+/-0.371) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-06}
0.630 (+/-0.312) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-05}
0.623 (+/-0.319) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 0.0001}
0.620 (+/-0.320) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-10}
0.797 (+/-0.492) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-09}
0.793 (+/-0.440) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-08}
0.687 (+/-0.434) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-07}
0.609 (+/-0.308) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-06}
0.630 (+/-0.312) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-05}
0.623 (+/-0.319) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 0.0001}
0.620 (+/-0.320) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-10}
0.848 (+/-0.460) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-09}
0.793 (+/-0.440) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-08}
0.687 (+/-0.434) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-07}
0.613 (+/-0.310) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-06}
0.628 (+/-0.313) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-05}
0.624 (+/-0.318) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 0.0001}
0.620 (+/-0.320) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.690 (+/-0.300) for {'C': 1.0, 'kernel': 'linear'}
0.659 (+/-0.287) for {'C': 10.0, 'kernel': 'linear'}
0.648 (+/-0.315) for {'C': 100.0, 'kernel': 'linear'}
0.631 (+/-0.312) for {'C': 1000.0, 'kernel': 'linear'}
0.619 (+/-0.321) for {'C': 10000.0, 'kernel': 'linear'}
0.620 (+/-0.321) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [1]
检查交叉验证集中测试集标签是否有预测不到的值: {-1}
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 1.00 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-09}
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-08}
0.699 (+/-0.308) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-07}
0.699 (+/-0.307) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-06}
0.698 (+/-0.306) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-05}
0.662 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 0.0001}
0.659 (+/-0.313) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-09}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-08}
0.699 (+/-0.308) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-07}
0.699 (+/-0.307) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-06}
0.698 (+/-0.306) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-05}
0.662 (+/-0.316) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 0.0001}
0.633 (+/-0.319) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-09}
0.617 (+/-0.238) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-08}
0.699 (+/-0.309) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-07}
0.699 (+/-0.306) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-06}
0.697 (+/-0.307) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-05}
0.661 (+/-0.316) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 0.0001}
0.633 (+/-0.319) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-09}
0.617 (+/-0.238) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-08}
0.699 (+/-0.308) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-07}
0.697 (+/-0.303) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-06}
0.663 (+/-0.313) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-05}
0.660 (+/-0.317) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 0.0001}
0.633 (+/-0.318) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-09}
0.642 (+/-0.236) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-08}
0.699 (+/-0.308) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-07}
0.696 (+/-0.300) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-06}
0.663 (+/-0.314) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-05}
0.660 (+/-0.317) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 0.0001}
0.633 (+/-0.316) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-09}
0.683 (+/-0.296) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-08}
0.699 (+/-0.309) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-07}
0.696 (+/-0.299) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-06}
0.662 (+/-0.314) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-05}
0.660 (+/-0.317) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 0.0001}
0.634 (+/-0.317) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-09}
0.683 (+/-0.296) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-08}
0.682 (+/-0.294) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-07}
0.680 (+/-0.288) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-06}
0.661 (+/-0.313) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-05}
0.635 (+/-0.327) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 0.0001}
0.635 (+/-0.320) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-10}
0.617 (+/-0.238) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-09}
0.700 (+/-0.308) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-08}
0.664 (+/-0.309) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-07}
0.663 (+/-0.309) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-06}
0.661 (+/-0.314) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-05}
0.635 (+/-0.327) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.0001}
0.635 (+/-0.320) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-10}
0.617 (+/-0.238) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-09}
0.699 (+/-0.307) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-08}
0.661 (+/-0.297) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-07}
0.663 (+/-0.308) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-06}
0.661 (+/-0.314) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-05}
0.634 (+/-0.327) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 0.0001}
0.636 (+/-0.320) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-10}
0.642 (+/-0.236) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-09}
0.699 (+/-0.307) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-08}
0.658 (+/-0.287) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-07}
0.638 (+/-0.317) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-06}
0.661 (+/-0.314) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-05}
0.634 (+/-0.327) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 0.0001}
0.636 (+/-0.320) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-10}
0.683 (+/-0.296) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-09}
0.699 (+/-0.307) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-08}
0.657 (+/-0.283) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-07}
0.638 (+/-0.318) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-06}
0.660 (+/-0.314) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-05}
0.634 (+/-0.329) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 0.0001}
0.636 (+/-0.320) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.699 (+/-0.307) for {'C': 1.0, 'kernel': 'linear'}
0.698 (+/-0.306) for {'C': 10.0, 'kernel': 'linear'}
0.664 (+/-0.315) for {'C': 100.0, 'kernel': 'linear'}
0.661 (+/-0.316) for {'C': 1000.0, 'kernel': 'linear'}
0.634 (+/-0.319) for {'C': 10000.0, 'kernel': 'linear'}
0.635 (+/-0.321) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-08}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6995187154753071
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-09}
0.647 (+/-0.460) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-08}
0.668 (+/-0.291) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-07}
0.613 (+/-0.189) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-06}
0.656 (+/-0.303) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-05}
0.626 (+/-0.314) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 0.0001}
0.625 (+/-0.315) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-09}
0.747 (+/-0.502) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-08}
0.669 (+/-0.296) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-07}
0.646 (+/-0.367) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-06}
0.652 (+/-0.308) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-05}
0.627 (+/-0.312) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 0.0001}
0.618 (+/-0.322) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-09}
0.747 (+/-0.502) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-08}
0.684 (+/-0.362) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-07}
0.610 (+/-0.283) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-06}
0.650 (+/-0.310) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-05}
0.627 (+/-0.313) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 0.0001}
0.619 (+/-0.322) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-09}
0.848 (+/-0.459) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-08}
0.608 (+/-0.220) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-07}
0.615 (+/-0.280) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-06}
0.624 (+/-0.315) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-05}
0.627 (+/-0.313) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 0.0001}
0.618 (+/-0.322) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-09}
0.848 (+/-0.460) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-08}
0.564 (+/-0.146) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-07}
0.612 (+/-0.281) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-06}
0.624 (+/-0.315) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-05}
0.627 (+/-0.312) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 0.0001}
0.618 (+/-0.322) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-10}
0.697 (+/-0.492) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-09}
0.848 (+/-0.460) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-08}
0.543 (+/-0.142) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-07}
0.615 (+/-0.283) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-06}
0.625 (+/-0.315) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-05}
0.627 (+/-0.313) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 0.0001}
0.619 (+/-0.322) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-10}
0.747 (+/-0.502) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-09}
0.806 (+/-0.436) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-08}
0.538 (+/-0.143) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-07}
0.617 (+/-0.282) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-06}
0.625 (+/-0.314) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-05}
0.622 (+/-0.319) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 0.0001}
0.620 (+/-0.321) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-10}
0.747 (+/-0.502) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-09}
0.798 (+/-0.437) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-08}
0.533 (+/-0.145) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-07}
0.600 (+/-0.288) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-06}
0.625 (+/-0.314) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-05}
0.622 (+/-0.319) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.0001}
0.620 (+/-0.321) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-10}
0.848 (+/-0.459) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-09}
0.798 (+/-0.437) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-08}
0.557 (+/-0.294) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-07}
0.608 (+/-0.291) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-06}
0.625 (+/-0.315) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-05}
0.622 (+/-0.319) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 0.0001}
0.620 (+/-0.320) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-10}
0.848 (+/-0.460) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-09}
0.793 (+/-0.440) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-08}
0.557 (+/-0.294) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-07}
0.591 (+/-0.295) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-06}
0.625 (+/-0.315) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-05}
0.622 (+/-0.319) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 0.0001}
0.620 (+/-0.320) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-11}
0.697 (+/-0.492) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-10}
0.848 (+/-0.460) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-09}
0.793 (+/-0.440) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-08}
0.557 (+/-0.294) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-07}
0.595 (+/-0.298) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-06}
0.625 (+/-0.315) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-05}
0.623 (+/-0.319) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 0.0001}
0.620 (+/-0.320) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.798 (+/-0.437) for {'C': 0.1, 'kernel': 'linear'}
0.643 (+/-0.202) for {'C': 1.0, 'kernel': 'linear'}
0.632 (+/-0.282) for {'C': 10.0, 'kernel': 'linear'}
0.625 (+/-0.314) for {'C': 100.0, 'kernel': 'linear'}
0.628 (+/-0.313) for {'C': 1000.0, 'kernel': 'linear'}
0.619 (+/-0.321) for {'C': 10000.0, 'kernel': 'linear'}
0.620 (+/-0.321) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-09}
0.575 (+/-0.229) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-08}
0.698 (+/-0.308) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-07}
0.696 (+/-0.302) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-06}
0.695 (+/-0.307) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-05}
0.659 (+/-0.315) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 0.0001}
0.658 (+/-0.312) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-09}
0.617 (+/-0.238) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-08}
0.698 (+/-0.309) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-07}
0.694 (+/-0.295) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-06}
0.693 (+/-0.308) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-05}
0.660 (+/-0.314) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 0.0001}
0.633 (+/-0.319) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-09}
0.617 (+/-0.238) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-08}
0.681 (+/-0.296) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-07}
0.693 (+/-0.294) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-06}
0.691 (+/-0.309) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-05}
0.660 (+/-0.313) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 0.0001}
0.633 (+/-0.319) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-09}
0.658 (+/-0.217) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-08}
0.694 (+/-0.306) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-07}
0.694 (+/-0.294) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-06}
0.658 (+/-0.314) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-05}
0.659 (+/-0.315) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 0.0001}
0.633 (+/-0.318) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-09}
0.683 (+/-0.296) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-08}
0.687 (+/-0.298) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-07}
0.693 (+/-0.294) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-06}
0.657 (+/-0.315) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-05}
0.659 (+/-0.315) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 0.0001}
0.633 (+/-0.316) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-10}
0.600 (+/-0.245) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-09}
0.700 (+/-0.309) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-08}
0.672 (+/-0.284) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-07}
0.694 (+/-0.296) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-06}
0.657 (+/-0.316) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-05}
0.659 (+/-0.315) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 0.0001}
0.634 (+/-0.317) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-10}
0.617 (+/-0.238) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-09}
0.700 (+/-0.308) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-08}
0.670 (+/-0.247) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-07}
0.678 (+/-0.283) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-06}
0.657 (+/-0.316) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-05}
0.634 (+/-0.326) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 0.0001}
0.635 (+/-0.320) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-10}
0.617 (+/-0.238) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-09}
0.699 (+/-0.307) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-08}
0.646 (+/-0.236) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-07}
0.661 (+/-0.305) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-06}
0.657 (+/-0.317) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-05}
0.634 (+/-0.327) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.0001}
0.635 (+/-0.320) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-10}
0.658 (+/-0.217) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-09}
0.699 (+/-0.307) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-08}
0.660 (+/-0.251) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-07}
0.661 (+/-0.304) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-06}
0.657 (+/-0.317) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-05}
0.634 (+/-0.327) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 0.0001}
0.636 (+/-0.320) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-10}
0.683 (+/-0.296) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-09}
0.699 (+/-0.307) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-08}
0.653 (+/-0.246) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-07}
0.636 (+/-0.312) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-06}
0.656 (+/-0.318) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-05}
0.634 (+/-0.327) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 0.0001}
0.636 (+/-0.320) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-11}
0.600 (+/-0.245) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-10}
0.700 (+/-0.309) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-09}
0.699 (+/-0.307) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-08}
0.648 (+/-0.236) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-07}
0.636 (+/-0.312) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-06}
0.657 (+/-0.317) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-05}
0.634 (+/-0.328) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 0.0001}
0.636 (+/-0.320) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.700 (+/-0.309) for {'C': 0.1, 'kernel': 'linear'}
0.698 (+/-0.306) for {'C': 1.0, 'kernel': 'linear'}
0.696 (+/-0.303) for {'C': 10.0, 'kernel': 'linear'}
0.658 (+/-0.314) for {'C': 100.0, 'kernel': 'linear'}
0.660 (+/-0.315) for {'C': 1000.0, 'kernel': 'linear'}
0.634 (+/-0.319) for {'C': 10000.0, 'kernel': 'linear'}
0.635 (+/-0.321) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-08}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.7
循环迭代之前,delta is: [5.58793545e-09 0.00000000e+00]
SVC模型clf_op_iter的参数是: {'kernel': 'rbf', 'decision_function_shape': 'ovr', 'class_weight': {-1: 15}, 'tol': 0.001, 'gamma': 1e-08, 'random_state': 0, 'cache_size': 200, 'verbose': False, 'degree': 3, 'C': 9999999.999999994, 'probability': False, 'shrinking': True, 'coef0': 0.0, 'max_iter': -1}
训练模型SVC对训练样本的预测准确率: 0.8936924167257264
测试集中,预测为舞弊样本的有: (array([1248], dtype=int64),)
测试集中,实际为舞弊样本的有: (array([1246, 1247, 1248, 1249, 1250, 1251, 1252, 1253, 1254, 1255, 1256],
dtype=int64),)
预测的舞弊样本数目: 1
训练模型SVC对测试样本的预测准确率: 0.8936924167257264
以上是第37次特征筛选。
第37次特征筛选,AUC值是: 0.5454545454545454
X_train_iter_svc.shape is: (1257, 15)
X_test_iter_svc.shape is: (1257, 15)
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.638 (+/-0.382) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.665 (+/-0.225) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.613 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.698 (+/-0.307) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.17 0.17 0.17 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6983958900156649
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.647 (+/-0.212) for {'C': 1.0, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.616 (+/-0.302) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.17 0.17 0.17 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.698 (+/-0.306) for {'C': 1.0, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.659 (+/-0.316) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.17 0.17 0.17 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6979151207848958
RBF的粗grid search最佳超参: {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001} RBF的粗grid search最高分值: 0.6983958900156649
linear的粗grid search最佳超参: {'C': 1.0, 'kernel': 'linear'} linear的粗grid search最高分值: 0.6979151207848958
粗grid search得到的parameter是:
[{'C': array([1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02, 1.e+03, 1.e+04, 1.e+05,
1.e+06, 1.e+07, 1.e+08]), 'kernel': ['rbf'], 'gamma': array([1.e-08, 1.e-07, 1.e-06, 1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01,
1.e+00, 1.e+01, 1.e+02])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.848 (+/-0.459) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.848 (+/-0.459) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.690 (+/-0.300) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.622 (+/-0.310) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.848 (+/-0.459) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.665 (+/-0.225) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.677 (+/-0.308) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.636 (+/-0.383) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.848 (+/-0.459) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.665 (+/-0.225) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.660 (+/-0.290) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.601 (+/-0.315) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.638 (+/-0.382) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.848 (+/-0.459) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.665 (+/-0.225) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.655 (+/-0.286) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.619 (+/-0.300) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.608 (+/-0.309) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.638 (+/-0.382) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.848 (+/-0.459) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.665 (+/-0.225) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.655 (+/-0.286) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.619 (+/-0.300) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.605 (+/-0.311) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.609 (+/-0.309) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.638 (+/-0.382) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.748 (+/-0.396) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.660 (+/-0.219) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.655 (+/-0.286) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.618 (+/-0.300) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.612 (+/-0.304) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.604 (+/-0.312) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.609 (+/-0.309) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.638 (+/-0.382) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.848 (+/-0.460) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.715 (+/-0.416) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.648 (+/-0.362) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.610 (+/-0.289) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.615 (+/-0.302) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.607 (+/-0.310) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.605 (+/-0.311) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.609 (+/-0.309) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.638 (+/-0.382) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.793 (+/-0.440) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.700 (+/-0.421) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.638 (+/-0.372) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.608 (+/-0.291) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.613 (+/-0.303) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.607 (+/-0.309) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.605 (+/-0.311) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.609 (+/-0.309) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.638 (+/-0.382) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.690 (+/-0.300) for {'C': 1.0, 'kernel': 'linear'}
0.666 (+/-0.357) for {'C': 10.0, 'kernel': 'linear'}
0.629 (+/-0.298) for {'C': 100.0, 'kernel': 'linear'}
0.617 (+/-0.301) for {'C': 1000.0, 'kernel': 'linear'}
0.608 (+/-0.309) for {'C': 10000.0, 'kernel': 'linear'}
0.607 (+/-0.309) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [1]
检查交叉验证集中测试集标签是否有预测不到的值: {-1}
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 1.00 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.658 (+/-0.217) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.658 (+/-0.217) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.699 (+/-0.307) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.497 (+/-0.008) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.658 (+/-0.217) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.307) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.698 (+/-0.307) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.612 (+/-0.240) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.007) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.658 (+/-0.217) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.698 (+/-0.307) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.306) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.587 (+/-0.233) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.007) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.658 (+/-0.217) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.698 (+/-0.307) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.306) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.662 (+/-0.313) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.007) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.658 (+/-0.217) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.698 (+/-0.307) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.306) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.661 (+/-0.314) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.610 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.007) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.699 (+/-0.308) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.698 (+/-0.306) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.697 (+/-0.306) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.662 (+/-0.312) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.658 (+/-0.310) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.609 (+/-0.238) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.240) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.239) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.007) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.683 (+/-0.296) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.676 (+/-0.243) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.695 (+/-0.302) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.661 (+/-0.312) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.660 (+/-0.313) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.632 (+/-0.321) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.609 (+/-0.241) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.240) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.239) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.007) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.699 (+/-0.307) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.654 (+/-0.243) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.662 (+/-0.311) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.660 (+/-0.314) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.659 (+/-0.312) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.633 (+/-0.323) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.609 (+/-0.241) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.240) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.239) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.007) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.699 (+/-0.307) for {'C': 1.0, 'kernel': 'linear'}
0.697 (+/-0.305) for {'C': 10.0, 'kernel': 'linear'}
0.663 (+/-0.314) for {'C': 100.0, 'kernel': 'linear'}
0.660 (+/-0.315) for {'C': 1000.0, 'kernel': 'linear'}
0.634 (+/-0.323) for {'C': 10000.0, 'kernel': 'linear'}
0.633 (+/-0.324) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6991982026547944
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.652 (+/-0.313) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.671 (+/-0.291) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.647 (+/-0.380) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.671 (+/-0.291) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.188) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.602 (+/-0.301) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.671 (+/-0.291) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.624 (+/-0.188) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.634 (+/-0.290) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.636 (+/-0.383) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.671 (+/-0.291) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.624 (+/-0.188) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.628 (+/-0.280) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.605 (+/-0.311) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.638 (+/-0.382) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.671 (+/-0.291) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.620 (+/-0.184) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.627 (+/-0.280) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.613 (+/-0.304) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.608 (+/-0.309) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.638 (+/-0.382) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.747 (+/-0.502) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.680 (+/-0.295) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.624 (+/-0.188) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.627 (+/-0.280) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.613 (+/-0.303) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.605 (+/-0.311) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.609 (+/-0.309) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.638 (+/-0.382) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.697 (+/-0.492) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.651 (+/-0.225) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.641 (+/-0.286) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.625 (+/-0.281) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.612 (+/-0.304) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.612 (+/-0.304) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.604 (+/-0.312) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.609 (+/-0.309) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.638 (+/-0.382) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.848 (+/-0.460) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.535 (+/-0.144) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.607 (+/-0.276) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.603 (+/-0.294) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.615 (+/-0.302) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.607 (+/-0.310) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.605 (+/-0.311) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.609 (+/-0.309) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.638 (+/-0.382) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.793 (+/-0.440) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.551 (+/-0.297) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.606 (+/-0.287) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.604 (+/-0.293) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.613 (+/-0.304) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.607 (+/-0.309) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.605 (+/-0.311) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.609 (+/-0.309) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.638 (+/-0.382) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.798 (+/-0.437) for {'C': 0.1, 'kernel': 'linear'}
0.647 (+/-0.212) for {'C': 1.0, 'kernel': 'linear'}
0.618 (+/-0.276) for {'C': 10.0, 'kernel': 'linear'}
0.612 (+/-0.304) for {'C': 100.0, 'kernel': 'linear'}
0.616 (+/-0.302) for {'C': 1000.0, 'kernel': 'linear'}
0.608 (+/-0.309) for {'C': 10000.0, 'kernel': 'linear'}
0.607 (+/-0.309) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.665 (+/-0.315) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.698 (+/-0.306) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.495 (+/-0.011) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.306) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.304) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.611 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.497 (+/-0.008) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.698 (+/-0.306) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.304) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.694 (+/-0.306) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.612 (+/-0.240) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.007) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.698 (+/-0.306) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.304) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.694 (+/-0.306) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.608 (+/-0.242) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.007) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.698 (+/-0.306) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.303) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.694 (+/-0.305) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.658 (+/-0.313) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.007) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.617 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.698 (+/-0.306) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.697 (+/-0.304) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.694 (+/-0.305) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.658 (+/-0.313) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.609 (+/-0.241) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.007) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.600 (+/-0.245) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.698 (+/-0.307) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.697 (+/-0.302) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.694 (+/-0.304) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.658 (+/-0.312) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.658 (+/-0.311) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.609 (+/-0.238) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.240) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.239) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.007) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.700 (+/-0.309) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.639 (+/-0.208) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.694 (+/-0.302) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.656 (+/-0.315) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.659 (+/-0.314) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.632 (+/-0.321) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.609 (+/-0.241) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.240) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.239) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.007) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.699 (+/-0.307) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.575 (+/-0.233) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.662 (+/-0.313) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.656 (+/-0.317) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.658 (+/-0.313) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.633 (+/-0.323) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.609 (+/-0.241) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.240) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.239) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.007) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.700 (+/-0.309) for {'C': 0.1, 'kernel': 'linear'}
0.698 (+/-0.306) for {'C': 1.0, 'kernel': 'linear'}
0.695 (+/-0.303) for {'C': 10.0, 'kernel': 'linear'}
0.658 (+/-0.314) for {'C': 100.0, 'kernel': 'linear'}
0.659 (+/-0.316) for {'C': 1000.0, 'kernel': 'linear'}
0.634 (+/-0.324) for {'C': 10000.0, 'kernel': 'linear'}
0.633 (+/-0.324) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.7
发现最优参数gamma为原先的最大/最小值,直接重新设置超参。
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-09}
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-08}
0.748 (+/-0.396) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-07}
0.660 (+/-0.219) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-06}
0.655 (+/-0.286) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-05}
0.618 (+/-0.300) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 0.0001}
0.612 (+/-0.304) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-09}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-08}
0.715 (+/-0.352) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-07}
0.692 (+/-0.352) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-06}
0.651 (+/-0.286) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-05}
0.616 (+/-0.302) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 0.0001}
0.607 (+/-0.310) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-09}
0.747 (+/-0.502) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-08}
0.740 (+/-0.392) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-07}
0.680 (+/-0.350) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-06}
0.648 (+/-0.292) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.301) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 0.0001}
0.606 (+/-0.310) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-09}
0.747 (+/-0.502) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-08}
0.710 (+/-0.352) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-07}
0.672 (+/-0.355) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-06}
0.629 (+/-0.298) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-05}
0.616 (+/-0.302) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 0.0001}
0.606 (+/-0.310) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-09}
0.797 (+/-0.492) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-08}
0.716 (+/-0.414) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-07}
0.657 (+/-0.359) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-06}
0.613 (+/-0.288) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-05}
0.615 (+/-0.302) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 0.0001}
0.606 (+/-0.310) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-09}
0.848 (+/-0.460) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-08}
0.740 (+/-0.449) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-07}
0.648 (+/-0.362) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-06}
0.610 (+/-0.289) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-05}
0.615 (+/-0.302) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 0.0001}
0.607 (+/-0.310) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-09}
0.823 (+/-0.451) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-08}
0.688 (+/-0.374) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-07}
0.650 (+/-0.361) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-06}
0.610 (+/-0.290) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-05}
0.615 (+/-0.302) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 0.0001}
0.607 (+/-0.309) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-10}
0.747 (+/-0.502) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-09}
0.806 (+/-0.436) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-08}
0.701 (+/-0.420) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-07}
0.647 (+/-0.363) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-06}
0.609 (+/-0.290) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-05}
0.615 (+/-0.302) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.0001}
0.607 (+/-0.310) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-10}
0.747 (+/-0.502) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-09}
0.798 (+/-0.437) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-08}
0.701 (+/-0.420) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-07}
0.636 (+/-0.373) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-06}
0.609 (+/-0.290) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-05}
0.614 (+/-0.303) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 0.0001}
0.607 (+/-0.309) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-10}
0.797 (+/-0.492) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-09}
0.793 (+/-0.440) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-08}
0.725 (+/-0.457) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-07}
0.637 (+/-0.373) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-06}
0.608 (+/-0.291) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-05}
0.614 (+/-0.303) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 0.0001}
0.607 (+/-0.309) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-10}
0.848 (+/-0.460) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-09}
0.793 (+/-0.440) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-08}
0.700 (+/-0.421) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-07}
0.638 (+/-0.372) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-06}
0.608 (+/-0.291) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-05}
0.613 (+/-0.303) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 0.0001}
0.607 (+/-0.309) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.690 (+/-0.300) for {'C': 1.0, 'kernel': 'linear'}
0.666 (+/-0.357) for {'C': 10.0, 'kernel': 'linear'}
0.629 (+/-0.298) for {'C': 100.0, 'kernel': 'linear'}
0.617 (+/-0.301) for {'C': 1000.0, 'kernel': 'linear'}
0.608 (+/-0.309) for {'C': 10000.0, 'kernel': 'linear'}
0.607 (+/-0.309) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [1]
检查交叉验证集中测试集标签是否有预测不到的值: {-1}
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 1.00 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-09}
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-08}
0.699 (+/-0.308) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-07}
0.698 (+/-0.306) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-06}
0.697 (+/-0.306) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-05}
0.662 (+/-0.312) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 0.0001}
0.658 (+/-0.310) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-09}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-08}
0.699 (+/-0.307) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-07}
0.698 (+/-0.306) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-06}
0.697 (+/-0.306) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-05}
0.661 (+/-0.313) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 0.0001}
0.633 (+/-0.321) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-09}
0.617 (+/-0.238) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-08}
0.699 (+/-0.308) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-07}
0.698 (+/-0.305) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-06}
0.681 (+/-0.294) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-05}
0.661 (+/-0.313) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 0.0001}
0.633 (+/-0.319) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-09}
0.617 (+/-0.238) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-08}
0.699 (+/-0.306) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-07}
0.697 (+/-0.304) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-06}
0.663 (+/-0.314) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-05}
0.660 (+/-0.313) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 0.0001}
0.633 (+/-0.320) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-09}
0.642 (+/-0.236) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-08}
0.690 (+/-0.276) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-07}
0.696 (+/-0.302) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-06}
0.662 (+/-0.312) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-05}
0.660 (+/-0.313) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 0.0001}
0.633 (+/-0.319) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-09}
0.683 (+/-0.296) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-08}
0.672 (+/-0.237) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-07}
0.695 (+/-0.302) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-06}
0.661 (+/-0.312) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-05}
0.660 (+/-0.313) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 0.0001}
0.632 (+/-0.321) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-09}
0.683 (+/-0.296) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-08}
0.663 (+/-0.233) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-07}
0.696 (+/-0.303) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-06}
0.661 (+/-0.312) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-05}
0.660 (+/-0.313) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 0.0001}
0.634 (+/-0.322) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-10}
0.617 (+/-0.238) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-09}
0.700 (+/-0.308) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-08}
0.658 (+/-0.238) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-07}
0.679 (+/-0.291) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-06}
0.661 (+/-0.313) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-05}
0.660 (+/-0.314) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.0001}
0.633 (+/-0.322) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-10}
0.617 (+/-0.238) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-09}
0.699 (+/-0.307) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-08}
0.658 (+/-0.239) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-07}
0.662 (+/-0.312) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-06}
0.661 (+/-0.313) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-05}
0.659 (+/-0.313) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 0.0001}
0.633 (+/-0.324) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-10}
0.642 (+/-0.236) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-09}
0.699 (+/-0.307) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-08}
0.653 (+/-0.244) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-07}
0.662 (+/-0.311) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-06}
0.660 (+/-0.314) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-05}
0.659 (+/-0.313) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 0.0001}
0.633 (+/-0.324) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-10}
0.683 (+/-0.296) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-09}
0.699 (+/-0.307) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-08}
0.652 (+/-0.245) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-07}
0.662 (+/-0.311) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-06}
0.660 (+/-0.314) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-05}
0.659 (+/-0.312) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 0.0001}
0.633 (+/-0.323) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.699 (+/-0.307) for {'C': 1.0, 'kernel': 'linear'}
0.697 (+/-0.305) for {'C': 10.0, 'kernel': 'linear'}
0.663 (+/-0.314) for {'C': 100.0, 'kernel': 'linear'}
0.660 (+/-0.315) for {'C': 1000.0, 'kernel': 'linear'}
0.634 (+/-0.323) for {'C': 10000.0, 'kernel': 'linear'}
0.633 (+/-0.324) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-08}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6995187154753071
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-09}
0.697 (+/-0.492) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-08}
0.651 (+/-0.225) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-07}
0.641 (+/-0.286) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-06}
0.625 (+/-0.281) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-05}
0.612 (+/-0.304) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 0.0001}
0.612 (+/-0.304) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-09}
0.747 (+/-0.502) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-08}
0.635 (+/-0.201) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-07}
0.641 (+/-0.365) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-06}
0.631 (+/-0.289) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-05}
0.613 (+/-0.303) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 0.0001}
0.607 (+/-0.310) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-09}
0.747 (+/-0.502) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-08}
0.618 (+/-0.188) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-07}
0.639 (+/-0.366) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-06}
0.625 (+/-0.288) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-05}
0.614 (+/-0.303) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 0.0001}
0.606 (+/-0.310) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-09}
0.848 (+/-0.459) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-08}
0.587 (+/-0.163) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-07}
0.607 (+/-0.277) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-06}
0.616 (+/-0.301) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-05}
0.614 (+/-0.302) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 0.0001}
0.606 (+/-0.310) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-09}
0.848 (+/-0.460) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-08}
0.553 (+/-0.147) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-07}
0.607 (+/-0.276) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-06}
0.606 (+/-0.291) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-05}
0.615 (+/-0.302) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 0.0001}
0.606 (+/-0.310) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-10}
0.697 (+/-0.492) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-09}
0.848 (+/-0.460) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-08}
0.535 (+/-0.144) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-07}
0.607 (+/-0.276) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-06}
0.603 (+/-0.294) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-05}
0.615 (+/-0.302) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 0.0001}
0.607 (+/-0.310) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-10}
0.747 (+/-0.502) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-09}
0.806 (+/-0.436) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-08}
0.530 (+/-0.146) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-07}
0.617 (+/-0.279) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-06}
0.603 (+/-0.294) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-05}
0.614 (+/-0.303) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 0.0001}
0.607 (+/-0.309) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-10}
0.747 (+/-0.502) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-09}
0.798 (+/-0.437) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-08}
0.528 (+/-0.147) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-07}
0.614 (+/-0.279) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-06}
0.603 (+/-0.293) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-05}
0.615 (+/-0.302) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.0001}
0.607 (+/-0.310) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-10}
0.848 (+/-0.459) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-09}
0.798 (+/-0.437) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-08}
0.551 (+/-0.297) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-07}
0.602 (+/-0.287) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-06}
0.604 (+/-0.293) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-05}
0.614 (+/-0.303) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 0.0001}
0.607 (+/-0.309) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-10}
0.848 (+/-0.460) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-09}
0.793 (+/-0.440) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-08}
0.550 (+/-0.298) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-07}
0.605 (+/-0.288) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-06}
0.604 (+/-0.293) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-05}
0.614 (+/-0.303) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 0.0001}
0.607 (+/-0.309) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-11}
0.697 (+/-0.492) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-10}
0.848 (+/-0.460) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-09}
0.793 (+/-0.440) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-08}
0.551 (+/-0.297) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-07}
0.606 (+/-0.287) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-06}
0.604 (+/-0.293) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-05}
0.613 (+/-0.304) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 0.0001}
0.607 (+/-0.309) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.798 (+/-0.437) for {'C': 0.1, 'kernel': 'linear'}
0.647 (+/-0.212) for {'C': 1.0, 'kernel': 'linear'}
0.618 (+/-0.276) for {'C': 10.0, 'kernel': 'linear'}
0.612 (+/-0.304) for {'C': 100.0, 'kernel': 'linear'}
0.616 (+/-0.302) for {'C': 1000.0, 'kernel': 'linear'}
0.608 (+/-0.309) for {'C': 10000.0, 'kernel': 'linear'}
0.607 (+/-0.309) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-09}
0.600 (+/-0.245) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-08}
0.698 (+/-0.307) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-07}
0.697 (+/-0.302) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-06}
0.694 (+/-0.304) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-05}
0.658 (+/-0.312) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 0.0001}
0.658 (+/-0.311) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-09}
0.617 (+/-0.238) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-08}
0.697 (+/-0.307) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-07}
0.695 (+/-0.300) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-06}
0.717 (+/-0.353) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-05}
0.659 (+/-0.312) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 0.0001}
0.633 (+/-0.322) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-09}
0.617 (+/-0.238) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-08}
0.696 (+/-0.308) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-07}
0.694 (+/-0.299) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-06}
0.700 (+/-0.347) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-05}
0.659 (+/-0.312) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 0.0001}
0.632 (+/-0.320) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-09}
0.658 (+/-0.217) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-08}
0.692 (+/-0.300) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-07}
0.695 (+/-0.302) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-06}
0.682 (+/-0.370) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-05}
0.659 (+/-0.313) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 0.0001}
0.632 (+/-0.321) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-09}
0.683 (+/-0.296) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-08}
0.677 (+/-0.265) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-07}
0.694 (+/-0.302) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-06}
0.681 (+/-0.370) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-05}
0.659 (+/-0.313) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 0.0001}
0.633 (+/-0.319) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-10}
0.600 (+/-0.245) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-09}
0.700 (+/-0.309) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-08}
0.639 (+/-0.208) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-07}
0.694 (+/-0.302) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-06}
0.656 (+/-0.315) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-05}
0.659 (+/-0.314) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 0.0001}
0.632 (+/-0.321) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-10}
0.617 (+/-0.238) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-09}
0.700 (+/-0.308) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-08}
0.621 (+/-0.143) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-07}
0.695 (+/-0.304) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-06}
0.655 (+/-0.314) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-05}
0.659 (+/-0.313) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 0.0001}
0.634 (+/-0.322) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-10}
0.617 (+/-0.238) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-09}
0.699 (+/-0.307) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-08}
0.589 (+/-0.141) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-07}
0.679 (+/-0.292) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-06}
0.655 (+/-0.315) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-05}
0.659 (+/-0.315) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.0001}
0.633 (+/-0.322) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-10}
0.658 (+/-0.217) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-09}
0.699 (+/-0.307) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-08}
0.567 (+/-0.124) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-07}
0.662 (+/-0.313) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-06}
0.656 (+/-0.317) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-05}
0.659 (+/-0.315) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 0.0001}
0.633 (+/-0.324) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-10}
0.683 (+/-0.296) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-09}
0.699 (+/-0.307) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-08}
0.555 (+/-0.126) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-07}
0.662 (+/-0.313) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-06}
0.656 (+/-0.317) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-05}
0.658 (+/-0.314) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 0.0001}
0.633 (+/-0.324) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-11}
0.600 (+/-0.245) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-10}
0.700 (+/-0.309) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-09}
0.699 (+/-0.307) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-08}
0.575 (+/-0.233) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-07}
0.662 (+/-0.313) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-06}
0.656 (+/-0.317) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-05}
0.658 (+/-0.313) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 0.0001}
0.633 (+/-0.323) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.700 (+/-0.309) for {'C': 0.1, 'kernel': 'linear'}
0.698 (+/-0.306) for {'C': 1.0, 'kernel': 'linear'}
0.695 (+/-0.303) for {'C': 10.0, 'kernel': 'linear'}
0.658 (+/-0.314) for {'C': 100.0, 'kernel': 'linear'}
0.659 (+/-0.316) for {'C': 1000.0, 'kernel': 'linear'}
0.634 (+/-0.324) for {'C': 10000.0, 'kernel': 'linear'}
0.633 (+/-0.324) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.05 0.17 0.08 6
1 0.99 0.97 0.98 623
avg / total 0.98 0.96 0.97 629
本轮grid search结果,得到最好的参数选择是: {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-05}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.7171453747217412
循环迭代之前,delta is: [8.41510681e+06 9.99000000e-06]
执行tunemodel()函数前,使用的grid search parameter是:
[{'C': array([1000000. , 1096478.19614319, 1202264.43461741,
1318256.73855641, 1445439.77074593, 1584893.19246111,
1737800.82874938, 1905460.71796325, 2089296.13085404,
2290867.65276777, 2511886.43150958]), 'kernel': ['rbf'], 'gamma': array([1.00000000e-06, 1.58489319e-06, 2.51188643e-06, 3.98107171e-06,
6.30957344e-06, 1.00000000e-05, 1.58489319e-05, 2.51188643e-05,
3.98107171e-05, 6.30957344e-05, 1.00000000e-04])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.660 (+/-0.219) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.664 (+/-0.291) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.659 (+/-0.287) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.649 (+/-0.288) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.643 (+/-0.285) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.655 (+/-0.286) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.646 (+/-0.284) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.641 (+/-0.286) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.637 (+/-0.307) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.620 (+/-0.299) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.618 (+/-0.300) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.660 (+/-0.219) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.659 (+/-0.287) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.630 (+/-0.189) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.666 (+/-0.357) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.643 (+/-0.285) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.643 (+/-0.279) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.649 (+/-0.286) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.628 (+/-0.299) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.637 (+/-0.307) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.620 (+/-0.299) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.617 (+/-0.301) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.680 (+/-0.294) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.659 (+/-0.287) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.655 (+/-0.286) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.634 (+/-0.289) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.635 (+/-0.277) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.651 (+/-0.286) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.649 (+/-0.286) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.628 (+/-0.299) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.637 (+/-0.307) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.619 (+/-0.300) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.617 (+/-0.301) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.671 (+/-0.290) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.659 (+/-0.287) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.655 (+/-0.286) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.634 (+/-0.289) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.633 (+/-0.277) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.651 (+/-0.286) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.651 (+/-0.286) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.625 (+/-0.298) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.632 (+/-0.301) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.619 (+/-0.300) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.617 (+/-0.301) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.667 (+/-0.292) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.656 (+/-0.288) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.653 (+/-0.287) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.641 (+/-0.288) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.633 (+/-0.277) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.646 (+/-0.284) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.644 (+/-0.285) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.623 (+/-0.298) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.624 (+/-0.299) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.619 (+/-0.300) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.617 (+/-0.301) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.692 (+/-0.352) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.653 (+/-0.287) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.648 (+/-0.287) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.641 (+/-0.288) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.641 (+/-0.286) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.651 (+/-0.286) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.639 (+/-0.284) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.623 (+/-0.298) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.627 (+/-0.300) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.619 (+/-0.300) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.616 (+/-0.302) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.684 (+/-0.350) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.653 (+/-0.287) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.648 (+/-0.287) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.643 (+/-0.287) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.643 (+/-0.285) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.655 (+/-0.287) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.639 (+/-0.284) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.623 (+/-0.298) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.623 (+/-0.299) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.619 (+/-0.300) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.616 (+/-0.302) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.684 (+/-0.350) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.653 (+/-0.287) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.636 (+/-0.287) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.632 (+/-0.278) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.639 (+/-0.285) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.651 (+/-0.286) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.637 (+/-0.284) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.623 (+/-0.298) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.624 (+/-0.299) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.618 (+/-0.301) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.616 (+/-0.302) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.684 (+/-0.350) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.653 (+/-0.287) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.632 (+/-0.289) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.632 (+/-0.278) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.639 (+/-0.285) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.655 (+/-0.287) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.641 (+/-0.284) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.623 (+/-0.298) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.624 (+/-0.299) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.617 (+/-0.302) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.617 (+/-0.302) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.680 (+/-0.350) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.651 (+/-0.287) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.632 (+/-0.289) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.632 (+/-0.278) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.642 (+/-0.287) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.660 (+/-0.292) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.629 (+/-0.298) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.621 (+/-0.299) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.623 (+/-0.300) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.617 (+/-0.302) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.617 (+/-0.301) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.680 (+/-0.350) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.641 (+/-0.287) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.635 (+/-0.288) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.632 (+/-0.278) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.645 (+/-0.286) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.648 (+/-0.292) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.625 (+/-0.298) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.620 (+/-0.299) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.623 (+/-0.300) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.617 (+/-0.302) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.617 (+/-0.301) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.690 (+/-0.300) for {'C': 1.0, 'kernel': 'linear'}
0.666 (+/-0.357) for {'C': 10.0, 'kernel': 'linear'}
0.629 (+/-0.298) for {'C': 100.0, 'kernel': 'linear'}
0.617 (+/-0.301) for {'C': 1000.0, 'kernel': 'linear'}
0.608 (+/-0.309) for {'C': 10000.0, 'kernel': 'linear'}
0.607 (+/-0.309) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.698 (+/-0.306) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.698 (+/-0.306) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.698 (+/-0.306) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.697 (+/-0.305) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.697 (+/-0.304) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.697 (+/-0.306) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.697 (+/-0.305) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.680 (+/-0.290) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.664 (+/-0.316) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.662 (+/-0.313) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.662 (+/-0.312) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.698 (+/-0.306) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.698 (+/-0.306) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.697 (+/-0.305) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.696 (+/-0.304) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.697 (+/-0.304) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.697 (+/-0.306) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.697 (+/-0.305) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.663 (+/-0.311) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.663 (+/-0.316) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.662 (+/-0.313) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.661 (+/-0.313) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.698 (+/-0.306) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.698 (+/-0.306) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.698 (+/-0.305) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.696 (+/-0.304) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.697 (+/-0.304) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.697 (+/-0.306) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.697 (+/-0.305) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.663 (+/-0.311) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.663 (+/-0.316) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.661 (+/-0.313) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.661 (+/-0.313) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.698 (+/-0.306) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.698 (+/-0.306) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.698 (+/-0.305) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.696 (+/-0.304) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.696 (+/-0.304) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.697 (+/-0.306) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.697 (+/-0.306) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.663 (+/-0.312) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.663 (+/-0.316) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.661 (+/-0.314) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.661 (+/-0.313) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.698 (+/-0.306) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.698 (+/-0.306) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.697 (+/-0.305) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.697 (+/-0.305) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.696 (+/-0.304) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.697 (+/-0.304) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.680 (+/-0.293) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.663 (+/-0.312) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.663 (+/-0.314) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.661 (+/-0.315) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.661 (+/-0.313) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.698 (+/-0.306) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.697 (+/-0.305) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.697 (+/-0.305) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.697 (+/-0.305) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.696 (+/-0.304) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.697 (+/-0.306) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.680 (+/-0.292) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.663 (+/-0.312) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.663 (+/-0.315) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.661 (+/-0.316) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.661 (+/-0.313) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.698 (+/-0.306) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.697 (+/-0.305) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.697 (+/-0.305) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.697 (+/-0.305) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.697 (+/-0.305) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.697 (+/-0.306) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.680 (+/-0.292) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.663 (+/-0.313) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.662 (+/-0.315) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.661 (+/-0.316) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.661 (+/-0.313) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.698 (+/-0.306) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.697 (+/-0.305) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.696 (+/-0.303) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.696 (+/-0.305) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.696 (+/-0.305) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.697 (+/-0.305) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.680 (+/-0.293) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.663 (+/-0.313) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.662 (+/-0.315) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.660 (+/-0.315) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.661 (+/-0.313) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.698 (+/-0.306) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.697 (+/-0.305) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.696 (+/-0.303) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.696 (+/-0.305) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.696 (+/-0.305) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.697 (+/-0.306) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.680 (+/-0.293) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.663 (+/-0.313) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.662 (+/-0.315) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.660 (+/-0.315) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.661 (+/-0.313) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.698 (+/-0.305) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.697 (+/-0.304) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.696 (+/-0.303) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.696 (+/-0.305) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.697 (+/-0.305) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.697 (+/-0.307) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.663 (+/-0.313) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.662 (+/-0.312) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.662 (+/-0.316) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.660 (+/-0.315) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.661 (+/-0.313) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.698 (+/-0.305) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.697 (+/-0.304) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.696 (+/-0.304) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.696 (+/-0.305) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.696 (+/-0.306) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.681 (+/-0.294) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.663 (+/-0.312) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.662 (+/-0.313) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.662 (+/-0.316) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.660 (+/-0.315) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.661 (+/-0.313) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.699 (+/-0.307) for {'C': 1.0, 'kernel': 'linear'}
0.697 (+/-0.305) for {'C': 10.0, 'kernel': 'linear'}
0.663 (+/-0.314) for {'C': 100.0, 'kernel': 'linear'}
0.660 (+/-0.315) for {'C': 1000.0, 'kernel': 'linear'}
0.634 (+/-0.323) for {'C': 10000.0, 'kernel': 'linear'}
0.633 (+/-0.324) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6985561464259213
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.641 (+/-0.286) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.616 (+/-0.290) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.611 (+/-0.279) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.609 (+/-0.276) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.624 (+/-0.278) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.625 (+/-0.281) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.630 (+/-0.289) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.628 (+/-0.290) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.615 (+/-0.302) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.612 (+/-0.304) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.612 (+/-0.304) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.627 (+/-0.287) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.618 (+/-0.290) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.586 (+/-0.153) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.609 (+/-0.276) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.622 (+/-0.278) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.621 (+/-0.280) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.632 (+/-0.292) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.616 (+/-0.301) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.615 (+/-0.302) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.612 (+/-0.304) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.612 (+/-0.304) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.645 (+/-0.362) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.616 (+/-0.291) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.613 (+/-0.279) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.611 (+/-0.276) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.621 (+/-0.279) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.628 (+/-0.291) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.633 (+/-0.292) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.616 (+/-0.301) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.615 (+/-0.302) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.612 (+/-0.304) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.612 (+/-0.304) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.644 (+/-0.363) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.617 (+/-0.290) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.610 (+/-0.276) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.615 (+/-0.278) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.620 (+/-0.280) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.632 (+/-0.288) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.632 (+/-0.292) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.616 (+/-0.301) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.614 (+/-0.302) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.611 (+/-0.304) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.613 (+/-0.304) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.642 (+/-0.364) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.613 (+/-0.292) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.610 (+/-0.276) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.622 (+/-0.278) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.620 (+/-0.280) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.631 (+/-0.289) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.624 (+/-0.290) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.615 (+/-0.301) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.615 (+/-0.302) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.611 (+/-0.304) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.613 (+/-0.303) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.641 (+/-0.365) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.614 (+/-0.292) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.611 (+/-0.278) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.621 (+/-0.279) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.626 (+/-0.291) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.631 (+/-0.289) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.624 (+/-0.290) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.615 (+/-0.301) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.615 (+/-0.302) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.611 (+/-0.305) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.613 (+/-0.303) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.641 (+/-0.365) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.606 (+/-0.280) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.609 (+/-0.279) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.623 (+/-0.278) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.630 (+/-0.288) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.634 (+/-0.291) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.623 (+/-0.291) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.612 (+/-0.304) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.611 (+/-0.300) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.611 (+/-0.305) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.613 (+/-0.303) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.640 (+/-0.366) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.606 (+/-0.280) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.609 (+/-0.279) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.620 (+/-0.278) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.630 (+/-0.288) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.634 (+/-0.291) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.623 (+/-0.290) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.612 (+/-0.304) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.615 (+/-0.301) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.612 (+/-0.304) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.613 (+/-0.303) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.638 (+/-0.367) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.604 (+/-0.281) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.614 (+/-0.278) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.620 (+/-0.278) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.632 (+/-0.288) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.633 (+/-0.291) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.626 (+/-0.292) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.612 (+/-0.304) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.612 (+/-0.304) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.612 (+/-0.304) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.613 (+/-0.303) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.638 (+/-0.367) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.604 (+/-0.281) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.614 (+/-0.278) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.617 (+/-0.280) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.634 (+/-0.291) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.633 (+/-0.291) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.611 (+/-0.305) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.612 (+/-0.304) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.612 (+/-0.304) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.612 (+/-0.304) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.614 (+/-0.303) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.639 (+/-0.366) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.606 (+/-0.280) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.618 (+/-0.278) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.617 (+/-0.280) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.633 (+/-0.292) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.625 (+/-0.288) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.611 (+/-0.305) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.612 (+/-0.304) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.615 (+/-0.301) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.612 (+/-0.304) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.614 (+/-0.303) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.798 (+/-0.437) for {'C': 0.1, 'kernel': 'linear'}
0.647 (+/-0.212) for {'C': 1.0, 'kernel': 'linear'}
0.618 (+/-0.276) for {'C': 10.0, 'kernel': 'linear'}
0.612 (+/-0.304) for {'C': 100.0, 'kernel': 'linear'}
0.616 (+/-0.302) for {'C': 1000.0, 'kernel': 'linear'}
0.608 (+/-0.309) for {'C': 10000.0, 'kernel': 'linear'}
0.607 (+/-0.309) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.697 (+/-0.302) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.694 (+/-0.298) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.694 (+/-0.300) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.694 (+/-0.302) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.695 (+/-0.304) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.694 (+/-0.304) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.717 (+/-0.352) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.700 (+/-0.346) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.682 (+/-0.367) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.657 (+/-0.314) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.658 (+/-0.312) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.696 (+/-0.301) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.695 (+/-0.299) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.694 (+/-0.300) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.694 (+/-0.302) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.695 (+/-0.305) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.693 (+/-0.305) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.717 (+/-0.351) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.683 (+/-0.367) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.682 (+/-0.367) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.657 (+/-0.314) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.658 (+/-0.312) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.695 (+/-0.300) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.694 (+/-0.298) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.694 (+/-0.300) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.695 (+/-0.302) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.695 (+/-0.304) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.693 (+/-0.305) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.717 (+/-0.353) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.683 (+/-0.367) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.682 (+/-0.367) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.657 (+/-0.314) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.658 (+/-0.311) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.695 (+/-0.300) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.694 (+/-0.298) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.694 (+/-0.301) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.695 (+/-0.302) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.694 (+/-0.304) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.718 (+/-0.353) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.717 (+/-0.352) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.683 (+/-0.368) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.682 (+/-0.366) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.657 (+/-0.313) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.658 (+/-0.312) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.695 (+/-0.300) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.693 (+/-0.296) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.694 (+/-0.301) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.695 (+/-0.304) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.694 (+/-0.304) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.717 (+/-0.353) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.699 (+/-0.345) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.682 (+/-0.368) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.682 (+/-0.367) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.657 (+/-0.313) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.659 (+/-0.313) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.695 (+/-0.300) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.693 (+/-0.296) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.694 (+/-0.301) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.695 (+/-0.304) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.694 (+/-0.304) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.717 (+/-0.353) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.699 (+/-0.346) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.682 (+/-0.368) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.682 (+/-0.367) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.657 (+/-0.313) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.659 (+/-0.312) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.695 (+/-0.300) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.693 (+/-0.296) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.694 (+/-0.300) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.695 (+/-0.304) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.718 (+/-0.352) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.717 (+/-0.355) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.698 (+/-0.345) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.657 (+/-0.314) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.682 (+/-0.368) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.657 (+/-0.312) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.659 (+/-0.312) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.694 (+/-0.299) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.693 (+/-0.296) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.694 (+/-0.302) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.695 (+/-0.304) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.718 (+/-0.352) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.717 (+/-0.355) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.698 (+/-0.347) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.657 (+/-0.314) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.682 (+/-0.368) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.657 (+/-0.314) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.659 (+/-0.312) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.694 (+/-0.299) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.693 (+/-0.296) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.694 (+/-0.304) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.694 (+/-0.304) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.718 (+/-0.352) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.717 (+/-0.354) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.698 (+/-0.346) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.657 (+/-0.314) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.657 (+/-0.314) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.657 (+/-0.314) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.659 (+/-0.312) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.694 (+/-0.300) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.693 (+/-0.296) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.694 (+/-0.304) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.694 (+/-0.305) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.718 (+/-0.352) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.717 (+/-0.355) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.656 (+/-0.313) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.657 (+/-0.314) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.657 (+/-0.314) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.657 (+/-0.313) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.659 (+/-0.312) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.694 (+/-0.299) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.693 (+/-0.296) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.695 (+/-0.305) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.694 (+/-0.305) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.717 (+/-0.352) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.700 (+/-0.347) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.656 (+/-0.313) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.657 (+/-0.314) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.683 (+/-0.367) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.657 (+/-0.313) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.659 (+/-0.312) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.700 (+/-0.309) for {'C': 0.1, 'kernel': 'linear'}
0.698 (+/-0.306) for {'C': 1.0, 'kernel': 'linear'}
0.695 (+/-0.303) for {'C': 10.0, 'kernel': 'linear'}
0.658 (+/-0.314) for {'C': 100.0, 'kernel': 'linear'}
0.659 (+/-0.316) for {'C': 1000.0, 'kernel': 'linear'}
0.634 (+/-0.324) for {'C': 10000.0, 'kernel': 'linear'}
0.633 (+/-0.324) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.06 0.17 0.09 6
1 0.99 0.98 0.98 623
avg / total 0.98 0.97 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.7182671695935362
第0轮loop中,tunemodel函数得到的最优参数是: ([{'C': array([1584893.19246111, 1614358.55682649, 1644371.72321493,
1674942.87602644, 1706082.38900313, 1737800.82874938,
1770108.95831742, 1803017.74085957, 1836538.34334835,
1870682.1403658 , 1905460.71796325]), 'kernel': ['rbf'], 'gamma': array([3.98107171e-06, 4.36515832e-06, 4.78630092e-06, 5.24807460e-06,
5.75439937e-06, 6.30957344e-06, 6.91830971e-06, 7.58577575e-06,
8.31763771e-06, 9.12010839e-06, 1.00000000e-05])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}], {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}, 0.7182671695935362)
这是第0次迭代微调C和gamma。
第0次迭代,得到delta: [1.52907636e+05 3.69042656e-06]
执行tunemodel()函数前,使用的grid search parameter是:
[{'C': array([1584893.19246111, 1614358.55682649, 1644371.72321493,
1674942.87602644, 1706082.38900313, 1737800.82874938,
1770108.95831742, 1803017.74085957, 1836538.34334835,
1870682.1403658 , 1905460.71796325]), 'kernel': ['rbf'], 'gamma': array([3.98107171e-06, 4.36515832e-06, 4.78630092e-06, 5.24807460e-06,
5.75439937e-06, 6.30957344e-06, 6.91830971e-06, 7.58577575e-06,
8.31763771e-06, 9.12010839e-06, 1.00000000e-05])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.641 (+/-0.288) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.628 (+/-0.279) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.643 (+/-0.285) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.639 (+/-0.287) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.658 (+/-0.290) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.641 (+/-0.286) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.633 (+/-0.276) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.651 (+/-0.292) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.639 (+/-0.287) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.630 (+/-0.279) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.651 (+/-0.286) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.641 (+/-0.288) for {'C': 1614358.5568264863, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.628 (+/-0.279) for {'C': 1614358.5568264863, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.641 (+/-0.286) for {'C': 1614358.5568264863, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.641 (+/-0.286) for {'C': 1614358.5568264863, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.683 (+/-0.354) for {'C': 1614358.5568264863, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.641 (+/-0.286) for {'C': 1614358.5568264863, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.633 (+/-0.276) for {'C': 1614358.5568264863, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.652 (+/-0.292) for {'C': 1614358.5568264863, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.638 (+/-0.287) for {'C': 1614358.5568264863, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.630 (+/-0.279) for {'C': 1614358.5568264863, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.651 (+/-0.286) for {'C': 1614358.5568264863, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.641 (+/-0.288) for {'C': 1644371.7232149309, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.628 (+/-0.279) for {'C': 1644371.7232149309, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.643 (+/-0.285) for {'C': 1644371.7232149309, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.641 (+/-0.286) for {'C': 1644371.7232149309, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.683 (+/-0.354) for {'C': 1644371.7232149309, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.643 (+/-0.285) for {'C': 1644371.7232149309, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.633 (+/-0.276) for {'C': 1644371.7232149309, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.643 (+/-0.285) for {'C': 1644371.7232149309, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.638 (+/-0.287) for {'C': 1644371.7232149309, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.638 (+/-0.288) for {'C': 1644371.7232149309, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.651 (+/-0.286) for {'C': 1644371.7232149309, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.641 (+/-0.288) for {'C': 1674942.8760264402, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.628 (+/-0.279) for {'C': 1674942.8760264402, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.643 (+/-0.285) for {'C': 1674942.8760264402, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.641 (+/-0.286) for {'C': 1674942.8760264402, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.666 (+/-0.305) for {'C': 1674942.8760264402, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.643 (+/-0.285) for {'C': 1674942.8760264402, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.637 (+/-0.277) for {'C': 1674942.8760264402, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.642 (+/-0.286) for {'C': 1674942.8760264402, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.638 (+/-0.287) for {'C': 1674942.8760264402, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.636 (+/-0.289) for {'C': 1674942.8760264402, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.651 (+/-0.286) for {'C': 1674942.8760264402, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.643 (+/-0.287) for {'C': 1706082.3890031257, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.628 (+/-0.279) for {'C': 1706082.3890031257, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.643 (+/-0.285) for {'C': 1706082.3890031257, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.641 (+/-0.286) for {'C': 1706082.3890031257, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.666 (+/-0.305) for {'C': 1706082.3890031257, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.643 (+/-0.285) for {'C': 1706082.3890031257, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.635 (+/-0.277) for {'C': 1706082.3890031257, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.642 (+/-0.286) for {'C': 1706082.3890031257, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.638 (+/-0.287) for {'C': 1706082.3890031257, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.636 (+/-0.288) for {'C': 1706082.3890031257, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.651 (+/-0.286) for {'C': 1706082.3890031257, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.643 (+/-0.287) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.628 (+/-0.279) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.645 (+/-0.286) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.641 (+/-0.286) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.669 (+/-0.305) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.643 (+/-0.285) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.635 (+/-0.277) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.644 (+/-0.285) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.634 (+/-0.287) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.636 (+/-0.288) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.655 (+/-0.287) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.643 (+/-0.287) for {'C': 1770108.958317422, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.628 (+/-0.279) for {'C': 1770108.958317422, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.643 (+/-0.285) for {'C': 1770108.958317422, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.641 (+/-0.286) for {'C': 1770108.958317422, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.694 (+/-0.355) for {'C': 1770108.958317422, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.643 (+/-0.285) for {'C': 1770108.958317422, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.634 (+/-0.277) for {'C': 1770108.958317422, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.640 (+/-0.286) for {'C': 1770108.958317422, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.634 (+/-0.287) for {'C': 1770108.958317422, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.636 (+/-0.289) for {'C': 1770108.958317422, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.651 (+/-0.286) for {'C': 1770108.958317422, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.635 (+/-0.279) for {'C': 1803017.7408595693, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.628 (+/-0.279) for {'C': 1803017.7408595693, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.643 (+/-0.285) for {'C': 1803017.7408595693, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.641 (+/-0.286) for {'C': 1803017.7408595693, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.694 (+/-0.355) for {'C': 1803017.7408595693, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.643 (+/-0.285) for {'C': 1803017.7408595693, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.634 (+/-0.277) for {'C': 1803017.7408595693, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.642 (+/-0.286) for {'C': 1803017.7408595693, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.635 (+/-0.286) for {'C': 1803017.7408595693, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.636 (+/-0.289) for {'C': 1803017.7408595693, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.651 (+/-0.286) for {'C': 1803017.7408595693, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.635 (+/-0.279) for {'C': 1836538.3433483506, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.624 (+/-0.278) for {'C': 1836538.3433483506, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.646 (+/-0.284) for {'C': 1836538.3433483506, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.645 (+/-0.286) for {'C': 1836538.3433483506, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.694 (+/-0.355) for {'C': 1836538.3433483506, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.643 (+/-0.285) for {'C': 1836538.3433483506, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.634 (+/-0.277) for {'C': 1836538.3433483506, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.642 (+/-0.286) for {'C': 1836538.3433483506, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.635 (+/-0.286) for {'C': 1836538.3433483506, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.636 (+/-0.289) for {'C': 1836538.3433483506, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.651 (+/-0.286) for {'C': 1836538.3433483506, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.635 (+/-0.279) for {'C': 1870682.140365804, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.624 (+/-0.278) for {'C': 1870682.140365804, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.644 (+/-0.285) for {'C': 1870682.140365804, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.637 (+/-0.278) for {'C': 1870682.140365804, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.694 (+/-0.355) for {'C': 1870682.140365804, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.639 (+/-0.285) for {'C': 1870682.140365804, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.645 (+/-0.287) for {'C': 1870682.140365804, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.647 (+/-0.284) for {'C': 1870682.140365804, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.634 (+/-0.287) for {'C': 1870682.140365804, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.636 (+/-0.289) for {'C': 1870682.140365804, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.651 (+/-0.286) for {'C': 1870682.140365804, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.632 (+/-0.278) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.624 (+/-0.278) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.644 (+/-0.285) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.637 (+/-0.278) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.694 (+/-0.355) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.639 (+/-0.285) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.645 (+/-0.287) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.647 (+/-0.284) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.634 (+/-0.287) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.636 (+/-0.289) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.651 (+/-0.286) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.690 (+/-0.300) for {'C': 1.0, 'kernel': 'linear'}
0.666 (+/-0.357) for {'C': 10.0, 'kernel': 'linear'}
0.629 (+/-0.298) for {'C': 100.0, 'kernel': 'linear'}
0.617 (+/-0.301) for {'C': 1000.0, 'kernel': 'linear'}
0.608 (+/-0.309) for {'C': 10000.0, 'kernel': 'linear'}
0.607 (+/-0.309) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.697 (+/-0.305) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.696 (+/-0.301) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.697 (+/-0.304) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.696 (+/-0.302) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.697 (+/-0.306) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.696 (+/-0.304) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.697 (+/-0.304) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.697 (+/-0.303) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.696 (+/-0.303) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.695 (+/-0.301) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.697 (+/-0.306) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.697 (+/-0.305) for {'C': 1614358.5568264863, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.696 (+/-0.301) for {'C': 1614358.5568264863, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.697 (+/-0.304) for {'C': 1614358.5568264863, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.697 (+/-0.302) for {'C': 1614358.5568264863, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.698 (+/-0.307) for {'C': 1614358.5568264863, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.696 (+/-0.304) for {'C': 1614358.5568264863, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.697 (+/-0.304) for {'C': 1614358.5568264863, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.697 (+/-0.303) for {'C': 1614358.5568264863, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.696 (+/-0.302) for {'C': 1614358.5568264863, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.695 (+/-0.301) for {'C': 1614358.5568264863, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.697 (+/-0.306) for {'C': 1614358.5568264863, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.696 (+/-0.305) for {'C': 1644371.7232149309, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.696 (+/-0.301) for {'C': 1644371.7232149309, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.697 (+/-0.304) for {'C': 1644371.7232149309, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.697 (+/-0.302) for {'C': 1644371.7232149309, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.698 (+/-0.307) for {'C': 1644371.7232149309, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.697 (+/-0.305) for {'C': 1644371.7232149309, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.697 (+/-0.304) for {'C': 1644371.7232149309, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.696 (+/-0.303) for {'C': 1644371.7232149309, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.696 (+/-0.302) for {'C': 1644371.7232149309, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.696 (+/-0.301) for {'C': 1644371.7232149309, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.697 (+/-0.306) for {'C': 1644371.7232149309, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.696 (+/-0.305) for {'C': 1674942.8760264402, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.696 (+/-0.301) for {'C': 1674942.8760264402, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.697 (+/-0.304) for {'C': 1674942.8760264402, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.697 (+/-0.302) for {'C': 1674942.8760264402, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.698 (+/-0.307) for {'C': 1674942.8760264402, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.697 (+/-0.305) for {'C': 1674942.8760264402, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.697 (+/-0.304) for {'C': 1674942.8760264402, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.696 (+/-0.302) for {'C': 1674942.8760264402, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.696 (+/-0.302) for {'C': 1674942.8760264402, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.695 (+/-0.301) for {'C': 1674942.8760264402, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.697 (+/-0.306) for {'C': 1674942.8760264402, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.697 (+/-0.305) for {'C': 1706082.3890031257, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.696 (+/-0.301) for {'C': 1706082.3890031257, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.697 (+/-0.304) for {'C': 1706082.3890031257, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.697 (+/-0.302) for {'C': 1706082.3890031257, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.698 (+/-0.307) for {'C': 1706082.3890031257, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.697 (+/-0.305) for {'C': 1706082.3890031257, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.697 (+/-0.304) for {'C': 1706082.3890031257, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.696 (+/-0.302) for {'C': 1706082.3890031257, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.696 (+/-0.302) for {'C': 1706082.3890031257, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.696 (+/-0.301) for {'C': 1706082.3890031257, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.697 (+/-0.306) for {'C': 1706082.3890031257, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.697 (+/-0.305) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.696 (+/-0.301) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.697 (+/-0.305) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.697 (+/-0.302) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.698 (+/-0.307) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.697 (+/-0.305) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.697 (+/-0.304) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.696 (+/-0.304) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.696 (+/-0.302) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.696 (+/-0.301) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.697 (+/-0.306) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.697 (+/-0.305) for {'C': 1770108.958317422, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.696 (+/-0.301) for {'C': 1770108.958317422, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.697 (+/-0.305) for {'C': 1770108.958317422, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.697 (+/-0.302) for {'C': 1770108.958317422, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.698 (+/-0.308) for {'C': 1770108.958317422, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.697 (+/-0.305) for {'C': 1770108.958317422, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.696 (+/-0.303) for {'C': 1770108.958317422, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.696 (+/-0.302) for {'C': 1770108.958317422, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.696 (+/-0.302) for {'C': 1770108.958317422, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.695 (+/-0.301) for {'C': 1770108.958317422, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.697 (+/-0.305) for {'C': 1770108.958317422, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.696 (+/-0.305) for {'C': 1803017.7408595693, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.696 (+/-0.301) for {'C': 1803017.7408595693, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.697 (+/-0.305) for {'C': 1803017.7408595693, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.697 (+/-0.302) for {'C': 1803017.7408595693, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.698 (+/-0.308) for {'C': 1803017.7408595693, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.697 (+/-0.305) for {'C': 1803017.7408595693, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.696 (+/-0.303) for {'C': 1803017.7408595693, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.696 (+/-0.303) for {'C': 1803017.7408595693, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.696 (+/-0.303) for {'C': 1803017.7408595693, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.695 (+/-0.301) for {'C': 1803017.7408595693, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.697 (+/-0.305) for {'C': 1803017.7408595693, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.696 (+/-0.305) for {'C': 1836538.3433483506, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.695 (+/-0.301) for {'C': 1836538.3433483506, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.697 (+/-0.305) for {'C': 1836538.3433483506, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.697 (+/-0.302) for {'C': 1836538.3433483506, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.698 (+/-0.308) for {'C': 1836538.3433483506, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.697 (+/-0.305) for {'C': 1836538.3433483506, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.696 (+/-0.303) for {'C': 1836538.3433483506, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.696 (+/-0.303) for {'C': 1836538.3433483506, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.696 (+/-0.303) for {'C': 1836538.3433483506, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.695 (+/-0.301) for {'C': 1836538.3433483506, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.697 (+/-0.305) for {'C': 1836538.3433483506, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.696 (+/-0.305) for {'C': 1870682.140365804, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.695 (+/-0.301) for {'C': 1870682.140365804, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.697 (+/-0.304) for {'C': 1870682.140365804, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.697 (+/-0.302) for {'C': 1870682.140365804, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.698 (+/-0.308) for {'C': 1870682.140365804, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.696 (+/-0.305) for {'C': 1870682.140365804, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.697 (+/-0.303) for {'C': 1870682.140365804, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.697 (+/-0.305) for {'C': 1870682.140365804, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.696 (+/-0.302) for {'C': 1870682.140365804, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.695 (+/-0.301) for {'C': 1870682.140365804, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.697 (+/-0.305) for {'C': 1870682.140365804, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.696 (+/-0.305) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.695 (+/-0.301) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.697 (+/-0.304) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.697 (+/-0.302) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.698 (+/-0.308) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.696 (+/-0.305) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.697 (+/-0.303) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.697 (+/-0.305) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.696 (+/-0.302) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.695 (+/-0.301) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.697 (+/-0.305) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.699 (+/-0.307) for {'C': 1.0, 'kernel': 'linear'}
0.697 (+/-0.305) for {'C': 10.0, 'kernel': 'linear'}
0.663 (+/-0.314) for {'C': 100.0, 'kernel': 'linear'}
0.660 (+/-0.315) for {'C': 1000.0, 'kernel': 'linear'}
0.634 (+/-0.323) for {'C': 10000.0, 'kernel': 'linear'}
0.633 (+/-0.324) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6985561464259213
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.621 (+/-0.279) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.621 (+/-0.279) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.625 (+/-0.278) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.621 (+/-0.279) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.627 (+/-0.279) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.626 (+/-0.291) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.620 (+/-0.281) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.627 (+/-0.291) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.623 (+/-0.294) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.623 (+/-0.283) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.631 (+/-0.289) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.621 (+/-0.279) for {'C': 1614358.5568264863, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.621 (+/-0.279) for {'C': 1614358.5568264863, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.625 (+/-0.278) for {'C': 1614358.5568264863, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.624 (+/-0.278) for {'C': 1614358.5568264863, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.625 (+/-0.280) for {'C': 1614358.5568264863, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.627 (+/-0.291) for {'C': 1614358.5568264863, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.620 (+/-0.281) for {'C': 1614358.5568264863, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.629 (+/-0.291) for {'C': 1614358.5568264863, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.623 (+/-0.294) for {'C': 1614358.5568264863, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.623 (+/-0.283) for {'C': 1614358.5568264863, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.631 (+/-0.289) for {'C': 1614358.5568264863, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.621 (+/-0.279) for {'C': 1644371.7232149309, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.621 (+/-0.279) for {'C': 1644371.7232149309, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.625 (+/-0.278) for {'C': 1644371.7232149309, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.622 (+/-0.279) for {'C': 1644371.7232149309, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.627 (+/-0.279) for {'C': 1644371.7232149309, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.627 (+/-0.291) for {'C': 1644371.7232149309, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.620 (+/-0.281) for {'C': 1644371.7232149309, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.628 (+/-0.293) for {'C': 1644371.7232149309, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.623 (+/-0.294) for {'C': 1644371.7232149309, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.631 (+/-0.293) for {'C': 1644371.7232149309, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.631 (+/-0.289) for {'C': 1644371.7232149309, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.621 (+/-0.279) for {'C': 1674942.8760264402, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.621 (+/-0.279) for {'C': 1674942.8760264402, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.625 (+/-0.278) for {'C': 1674942.8760264402, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.622 (+/-0.279) for {'C': 1674942.8760264402, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.627 (+/-0.279) for {'C': 1674942.8760264402, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.627 (+/-0.291) for {'C': 1674942.8760264402, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.624 (+/-0.282) for {'C': 1674942.8760264402, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.627 (+/-0.294) for {'C': 1674942.8760264402, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.623 (+/-0.294) for {'C': 1674942.8760264402, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.630 (+/-0.293) for {'C': 1674942.8760264402, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.631 (+/-0.289) for {'C': 1674942.8760264402, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.623 (+/-0.278) for {'C': 1706082.3890031257, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.621 (+/-0.279) for {'C': 1706082.3890031257, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.625 (+/-0.278) for {'C': 1706082.3890031257, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.622 (+/-0.279) for {'C': 1706082.3890031257, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.626 (+/-0.280) for {'C': 1706082.3890031257, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.630 (+/-0.288) for {'C': 1706082.3890031257, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.624 (+/-0.282) for {'C': 1706082.3890031257, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.628 (+/-0.293) for {'C': 1706082.3890031257, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.623 (+/-0.294) for {'C': 1706082.3890031257, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.630 (+/-0.294) for {'C': 1706082.3890031257, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.631 (+/-0.289) for {'C': 1706082.3890031257, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.623 (+/-0.278) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.621 (+/-0.279) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.625 (+/-0.278) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.622 (+/-0.279) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.628 (+/-0.281) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.630 (+/-0.288) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.624 (+/-0.283) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.628 (+/-0.293) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.623 (+/-0.294) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.630 (+/-0.294) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.634 (+/-0.291) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.623 (+/-0.278) for {'C': 1770108.958317422, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.621 (+/-0.279) for {'C': 1770108.958317422, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.625 (+/-0.278) for {'C': 1770108.958317422, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.622 (+/-0.279) for {'C': 1770108.958317422, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.635 (+/-0.290) for {'C': 1770108.958317422, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.630 (+/-0.288) for {'C': 1770108.958317422, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.624 (+/-0.283) for {'C': 1770108.958317422, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.625 (+/-0.294) for {'C': 1770108.958317422, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.623 (+/-0.294) for {'C': 1770108.958317422, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.630 (+/-0.294) for {'C': 1770108.958317422, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.634 (+/-0.291) for {'C': 1770108.958317422, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.623 (+/-0.278) for {'C': 1803017.7408595693, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.621 (+/-0.279) for {'C': 1803017.7408595693, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.625 (+/-0.278) for {'C': 1803017.7408595693, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.622 (+/-0.279) for {'C': 1803017.7408595693, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.634 (+/-0.290) for {'C': 1803017.7408595693, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.630 (+/-0.288) for {'C': 1803017.7408595693, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.624 (+/-0.283) for {'C': 1803017.7408595693, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.627 (+/-0.294) for {'C': 1803017.7408595693, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.623 (+/-0.294) for {'C': 1803017.7408595693, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.630 (+/-0.294) for {'C': 1803017.7408595693, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.634 (+/-0.291) for {'C': 1803017.7408595693, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.623 (+/-0.278) for {'C': 1836538.3433483506, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.621 (+/-0.279) for {'C': 1836538.3433483506, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.628 (+/-0.277) for {'C': 1836538.3433483506, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.627 (+/-0.280) for {'C': 1836538.3433483506, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.633 (+/-0.291) for {'C': 1836538.3433483506, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.626 (+/-0.292) for {'C': 1836538.3433483506, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.624 (+/-0.283) for {'C': 1836538.3433483506, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.627 (+/-0.294) for {'C': 1836538.3433483506, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.623 (+/-0.294) for {'C': 1836538.3433483506, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.629 (+/-0.294) for {'C': 1836538.3433483506, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.634 (+/-0.291) for {'C': 1836538.3433483506, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.620 (+/-0.278) for {'C': 1870682.140365804, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.621 (+/-0.279) for {'C': 1870682.140365804, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.625 (+/-0.278) for {'C': 1870682.140365804, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.627 (+/-0.280) for {'C': 1870682.140365804, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.633 (+/-0.291) for {'C': 1870682.140365804, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.630 (+/-0.288) for {'C': 1870682.140365804, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.636 (+/-0.294) for {'C': 1870682.140365804, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.627 (+/-0.294) for {'C': 1870682.140365804, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.623 (+/-0.294) for {'C': 1870682.140365804, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.630 (+/-0.294) for {'C': 1870682.140365804, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.634 (+/-0.291) for {'C': 1870682.140365804, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.620 (+/-0.278) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.620 (+/-0.280) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.625 (+/-0.278) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.627 (+/-0.280) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.633 (+/-0.291) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.630 (+/-0.288) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.636 (+/-0.294) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.627 (+/-0.294) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.623 (+/-0.294) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.629 (+/-0.294) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.634 (+/-0.291) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.798 (+/-0.437) for {'C': 0.1, 'kernel': 'linear'}
0.647 (+/-0.212) for {'C': 1.0, 'kernel': 'linear'}
0.618 (+/-0.276) for {'C': 10.0, 'kernel': 'linear'}
0.612 (+/-0.304) for {'C': 100.0, 'kernel': 'linear'}
0.616 (+/-0.302) for {'C': 1000.0, 'kernel': 'linear'}
0.608 (+/-0.309) for {'C': 10000.0, 'kernel': 'linear'}
0.607 (+/-0.309) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.695 (+/-0.304) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.694 (+/-0.303) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.695 (+/-0.304) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.695 (+/-0.304) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.694 (+/-0.307) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.694 (+/-0.304) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.693 (+/-0.306) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.693 (+/-0.303) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.692 (+/-0.305) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.693 (+/-0.304) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.717 (+/-0.353) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.695 (+/-0.304) for {'C': 1614358.5568264863, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.694 (+/-0.303) for {'C': 1614358.5568264863, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.695 (+/-0.304) for {'C': 1614358.5568264863, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.695 (+/-0.304) for {'C': 1614358.5568264863, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.694 (+/-0.306) for {'C': 1614358.5568264863, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.694 (+/-0.305) for {'C': 1614358.5568264863, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.693 (+/-0.306) for {'C': 1614358.5568264863, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.693 (+/-0.303) for {'C': 1614358.5568264863, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.692 (+/-0.305) for {'C': 1614358.5568264863, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.693 (+/-0.304) for {'C': 1614358.5568264863, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.717 (+/-0.353) for {'C': 1614358.5568264863, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.695 (+/-0.304) for {'C': 1644371.7232149309, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.694 (+/-0.303) for {'C': 1644371.7232149309, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.695 (+/-0.304) for {'C': 1644371.7232149309, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.695 (+/-0.304) for {'C': 1644371.7232149309, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.694 (+/-0.307) for {'C': 1644371.7232149309, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.694 (+/-0.305) for {'C': 1644371.7232149309, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.693 (+/-0.306) for {'C': 1644371.7232149309, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.693 (+/-0.303) for {'C': 1644371.7232149309, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.692 (+/-0.305) for {'C': 1644371.7232149309, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.693 (+/-0.304) for {'C': 1644371.7232149309, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.717 (+/-0.354) for {'C': 1644371.7232149309, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.695 (+/-0.304) for {'C': 1674942.8760264402, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.694 (+/-0.303) for {'C': 1674942.8760264402, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.695 (+/-0.304) for {'C': 1674942.8760264402, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.695 (+/-0.304) for {'C': 1674942.8760264402, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.694 (+/-0.307) for {'C': 1674942.8760264402, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.694 (+/-0.305) for {'C': 1674942.8760264402, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.693 (+/-0.306) for {'C': 1674942.8760264402, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.693 (+/-0.303) for {'C': 1674942.8760264402, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.692 (+/-0.305) for {'C': 1674942.8760264402, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.693 (+/-0.304) for {'C': 1674942.8760264402, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.717 (+/-0.354) for {'C': 1674942.8760264402, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.695 (+/-0.304) for {'C': 1706082.3890031257, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.694 (+/-0.303) for {'C': 1706082.3890031257, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.695 (+/-0.304) for {'C': 1706082.3890031257, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.695 (+/-0.304) for {'C': 1706082.3890031257, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.694 (+/-0.307) for {'C': 1706082.3890031257, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.718 (+/-0.352) for {'C': 1706082.3890031257, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.693 (+/-0.306) for {'C': 1706082.3890031257, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.693 (+/-0.303) for {'C': 1706082.3890031257, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.692 (+/-0.305) for {'C': 1706082.3890031257, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.693 (+/-0.304) for {'C': 1706082.3890031257, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.717 (+/-0.354) for {'C': 1706082.3890031257, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.695 (+/-0.304) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.694 (+/-0.304) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.695 (+/-0.304) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.695 (+/-0.304) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.694 (+/-0.307) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.718 (+/-0.352) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.693 (+/-0.306) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.693 (+/-0.303) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.692 (+/-0.305) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.693 (+/-0.304) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.717 (+/-0.355) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.695 (+/-0.304) for {'C': 1770108.958317422, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.694 (+/-0.303) for {'C': 1770108.958317422, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.695 (+/-0.304) for {'C': 1770108.958317422, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.695 (+/-0.304) for {'C': 1770108.958317422, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.694 (+/-0.306) for {'C': 1770108.958317422, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.718 (+/-0.352) for {'C': 1770108.958317422, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.693 (+/-0.306) for {'C': 1770108.958317422, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.692 (+/-0.303) for {'C': 1770108.958317422, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.692 (+/-0.305) for {'C': 1770108.958317422, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.692 (+/-0.303) for {'C': 1770108.958317422, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.717 (+/-0.355) for {'C': 1770108.958317422, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.695 (+/-0.304) for {'C': 1803017.7408595693, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.694 (+/-0.303) for {'C': 1803017.7408595693, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.695 (+/-0.304) for {'C': 1803017.7408595693, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.695 (+/-0.304) for {'C': 1803017.7408595693, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.694 (+/-0.306) for {'C': 1803017.7408595693, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.718 (+/-0.351) for {'C': 1803017.7408595693, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.693 (+/-0.306) for {'C': 1803017.7408595693, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.692 (+/-0.304) for {'C': 1803017.7408595693, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.692 (+/-0.305) for {'C': 1803017.7408595693, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.692 (+/-0.303) for {'C': 1803017.7408595693, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.717 (+/-0.355) for {'C': 1803017.7408595693, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.695 (+/-0.304) for {'C': 1836538.3433483506, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.694 (+/-0.304) for {'C': 1836538.3433483506, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.695 (+/-0.304) for {'C': 1836538.3433483506, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.695 (+/-0.304) for {'C': 1836538.3433483506, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.694 (+/-0.306) for {'C': 1836538.3433483506, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.693 (+/-0.305) for {'C': 1836538.3433483506, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.693 (+/-0.306) for {'C': 1836538.3433483506, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.692 (+/-0.304) for {'C': 1836538.3433483506, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.692 (+/-0.305) for {'C': 1836538.3433483506, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.692 (+/-0.303) for {'C': 1836538.3433483506, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.717 (+/-0.355) for {'C': 1836538.3433483506, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.695 (+/-0.304) for {'C': 1870682.140365804, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.694 (+/-0.304) for {'C': 1870682.140365804, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.695 (+/-0.304) for {'C': 1870682.140365804, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.695 (+/-0.304) for {'C': 1870682.140365804, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.694 (+/-0.306) for {'C': 1870682.140365804, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.718 (+/-0.352) for {'C': 1870682.140365804, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.693 (+/-0.307) for {'C': 1870682.140365804, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.692 (+/-0.304) for {'C': 1870682.140365804, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.692 (+/-0.305) for {'C': 1870682.140365804, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.692 (+/-0.303) for {'C': 1870682.140365804, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.717 (+/-0.355) for {'C': 1870682.140365804, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.695 (+/-0.304) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.694 (+/-0.304) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.695 (+/-0.305) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.695 (+/-0.304) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.694 (+/-0.306) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.718 (+/-0.352) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.693 (+/-0.307) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.692 (+/-0.304) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.691 (+/-0.305) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.692 (+/-0.303) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.717 (+/-0.355) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.700 (+/-0.309) for {'C': 0.1, 'kernel': 'linear'}
0.698 (+/-0.306) for {'C': 1.0, 'kernel': 'linear'}
0.695 (+/-0.303) for {'C': 10.0, 'kernel': 'linear'}
0.658 (+/-0.314) for {'C': 100.0, 'kernel': 'linear'}
0.659 (+/-0.316) for {'C': 1000.0, 'kernel': 'linear'}
0.634 (+/-0.324) for {'C': 10000.0, 'kernel': 'linear'}
0.633 (+/-0.324) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.06 0.17 0.09 6
1 0.99 0.98 0.98 623
avg / total 0.98 0.97 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 1803017.7408595693, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.7184274260037926
第1轮loop中,tunemodel函数得到的最优参数是: ([{'C': array([1770108.95831742, 1776642.30820362, 1783199.77223292,
1789781.43940896, 1796387.39906389, 1803017.74085957,
1809672.55478879, 1816351.93117652, 1823055.96068107,
1829784.73429542, 1836538.34334835]), 'kernel': ['rbf'], 'gamma': array([5.75439937e-06, 5.86138165e-06, 5.97035287e-06, 6.08135001e-06,
6.19441075e-06, 6.30957344e-06, 6.42687717e-06, 6.54636174e-06,
6.66806769e-06, 6.79203633e-06, 6.91830971e-06])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}], {'C': 1803017.7408595693, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}, 0.7184274260037926)
这是第1次迭代微调C和gamma。
第1次迭代,得到delta: [65216.91211019 0. ]
SVC模型clf_op_iter的参数是: {'kernel': 'rbf', 'decision_function_shape': 'ovr', 'class_weight': {-1: 15}, 'tol': 0.001, 'gamma': 6.309573444801928e-06, 'random_state': 0, 'cache_size': 200, 'verbose': False, 'degree': 3, 'C': 1803017.7408595693, 'probability': False, 'shrinking': True, 'coef0': 0.0, 'max_iter': -1}
训练模型SVC对训练样本的预测准确率: 0.9532246633593197
测试集中,预测为舞弊样本的有: (array([ 0, 1, 2, ..., 1254, 1255, 1256], dtype=int64),)
测试集中,实际为舞弊样本的有: (array([1246, 1247, 1248, 1249, 1250, 1251, 1252, 1253, 1254, 1255, 1256],
dtype=int64),)
预测的舞弊样本数目: 1126
训练模型SVC对测试样本的预测准确率: 0.19844082211197733
以上是第38次特征筛选。
第38次特征筛选,AUC值是: 0.5067123887348606
X_train_iter_svc.shape is: (1257, 14)
X_test_iter_svc.shape is: (1257, 14)
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.722 (+/-0.473) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.638 (+/-0.382) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.646 (+/-0.204) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.613 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.698 (+/-0.306) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.17 0.17 0.17 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6979146054909721
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.635 (+/-0.198) for {'C': 1.0, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.589 (+/-0.194) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.14 0.17 0.15 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.698 (+/-0.305) for {'C': 1.0, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.659 (+/-0.314) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.14 0.17 0.15 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.697594607964383
RBF的粗grid search最佳超参: {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001} RBF的粗grid search最高分值: 0.6979146054909721
linear的粗grid search最佳超参: {'C': 1.0, 'kernel': 'linear'} linear的粗grid search最高分值: 0.697594607964383
粗grid search得到的parameter是:
[{'C': array([1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02, 1.e+03, 1.e+04, 1.e+05,
1.e+06, 1.e+07, 1.e+08]), 'kernel': ['rbf'], 'gamma': array([1.e-08, 1.e-07, 1.e-06, 1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01,
1.e+00, 1.e+01, 1.e+02])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.848 (+/-0.459) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.848 (+/-0.459) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.665 (+/-0.225) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.618 (+/-0.309) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.848 (+/-0.459) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.646 (+/-0.204) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.660 (+/-0.290) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.636 (+/-0.384) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.848 (+/-0.459) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.646 (+/-0.204) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.167) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.601 (+/-0.315) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.638 (+/-0.382) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.848 (+/-0.459) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.646 (+/-0.204) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.167) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.595 (+/-0.192) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.608 (+/-0.309) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.638 (+/-0.382) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.848 (+/-0.459) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.646 (+/-0.204) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.612 (+/-0.165) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.596 (+/-0.193) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.576 (+/-0.207) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.610 (+/-0.308) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.638 (+/-0.382) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.731 (+/-0.365) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.660 (+/-0.219) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.606 (+/-0.165) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.597 (+/-0.195) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.591 (+/-0.194) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.581 (+/-0.201) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.610 (+/-0.308) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.638 (+/-0.382) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.848 (+/-0.460) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.707 (+/-0.415) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.620 (+/-0.289) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.586 (+/-0.170) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.588 (+/-0.194) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.582 (+/-0.201) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.585 (+/-0.200) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.610 (+/-0.308) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.638 (+/-0.382) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.793 (+/-0.440) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.671 (+/-0.375) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.623 (+/-0.289) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.587 (+/-0.170) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.588 (+/-0.194) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.582 (+/-0.201) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.585 (+/-0.200) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.610 (+/-0.308) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.638 (+/-0.382) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.690 (+/-0.300) for {'C': 1.0, 'kernel': 'linear'}
0.613 (+/-0.190) for {'C': 10.0, 'kernel': 'linear'}
0.608 (+/-0.197) for {'C': 100.0, 'kernel': 'linear'}
0.594 (+/-0.195) for {'C': 1000.0, 'kernel': 'linear'}
0.586 (+/-0.195) for {'C': 10000.0, 'kernel': 'linear'}
0.582 (+/-0.201) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [1]
检查交叉验证集中测试集标签是否有预测不到的值: {-1}
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 1.00 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.658 (+/-0.217) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.658 (+/-0.217) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.698 (+/-0.307) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.236) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.497 (+/-0.008) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.658 (+/-0.217) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.306) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.698 (+/-0.306) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.611 (+/-0.241) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.007) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.658 (+/-0.217) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.698 (+/-0.306) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.305) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.587 (+/-0.232) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.007) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.658 (+/-0.217) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.698 (+/-0.306) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.305) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.662 (+/-0.314) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.007) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.658 (+/-0.217) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.698 (+/-0.306) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.305) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.662 (+/-0.315) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.585 (+/-0.235) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.007) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.699 (+/-0.308) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.698 (+/-0.306) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.696 (+/-0.301) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.662 (+/-0.315) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.660 (+/-0.313) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.611 (+/-0.239) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.240) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.239) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.007) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.683 (+/-0.296) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.660 (+/-0.221) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.695 (+/-0.301) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.661 (+/-0.310) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.658 (+/-0.312) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.633 (+/-0.320) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.611 (+/-0.241) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.240) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.239) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.007) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.699 (+/-0.307) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.654 (+/-0.242) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.695 (+/-0.302) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.661 (+/-0.312) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.658 (+/-0.313) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.633 (+/-0.321) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.611 (+/-0.241) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.240) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.239) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.007) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.699 (+/-0.307) for {'C': 1.0, 'kernel': 'linear'}
0.696 (+/-0.304) for {'C': 10.0, 'kernel': 'linear'}
0.663 (+/-0.314) for {'C': 100.0, 'kernel': 'linear'}
0.660 (+/-0.315) for {'C': 1000.0, 'kernel': 'linear'}
0.658 (+/-0.307) for {'C': 10000.0, 'kernel': 'linear'}
0.633 (+/-0.322) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6991976873608706
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.652 (+/-0.313) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.642 (+/-0.204) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.622 (+/-0.310) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.642 (+/-0.204) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.622 (+/-0.185) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.598 (+/-0.298) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.642 (+/-0.204) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.618 (+/-0.182) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.625 (+/-0.282) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.636 (+/-0.384) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.642 (+/-0.204) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.618 (+/-0.182) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.597 (+/-0.161) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.605 (+/-0.311) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.638 (+/-0.382) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.642 (+/-0.204) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.618 (+/-0.182) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.597 (+/-0.161) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.587 (+/-0.196) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.608 (+/-0.309) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.638 (+/-0.382) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.747 (+/-0.502) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.642 (+/-0.204) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.618 (+/-0.182) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.594 (+/-0.157) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.587 (+/-0.195) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.581 (+/-0.202) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.610 (+/-0.308) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.638 (+/-0.382) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.697 (+/-0.492) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.651 (+/-0.225) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.604 (+/-0.176) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.594 (+/-0.162) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.587 (+/-0.195) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.589 (+/-0.194) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.581 (+/-0.201) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.610 (+/-0.308) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.638 (+/-0.382) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.831 (+/-0.449) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.535 (+/-0.144) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.607 (+/-0.277) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.582 (+/-0.166) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.588 (+/-0.194) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.582 (+/-0.201) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.585 (+/-0.200) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.610 (+/-0.308) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.638 (+/-0.382) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.793 (+/-0.440) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.551 (+/-0.297) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.618 (+/-0.278) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.578 (+/-0.172) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.588 (+/-0.194) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.582 (+/-0.201) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.585 (+/-0.200) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.610 (+/-0.308) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.638 (+/-0.382) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.798 (+/-0.437) for {'C': 0.1, 'kernel': 'linear'}
0.635 (+/-0.198) for {'C': 1.0, 'kernel': 'linear'}
0.586 (+/-0.152) for {'C': 10.0, 'kernel': 'linear'}
0.587 (+/-0.195) for {'C': 100.0, 'kernel': 'linear'}
0.589 (+/-0.194) for {'C': 1000.0, 'kernel': 'linear'}
0.586 (+/-0.195) for {'C': 10000.0, 'kernel': 'linear'}
0.582 (+/-0.201) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.665 (+/-0.315) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.698 (+/-0.306) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.495 (+/-0.011) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.306) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.304) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.611 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.497 (+/-0.008) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.698 (+/-0.306) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.303) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.694 (+/-0.305) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.611 (+/-0.241) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.007) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.698 (+/-0.306) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.303) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.694 (+/-0.305) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.608 (+/-0.243) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.007) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.698 (+/-0.306) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.303) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.694 (+/-0.305) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.657 (+/-0.312) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.007) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.617 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.698 (+/-0.306) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.697 (+/-0.303) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.694 (+/-0.305) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.658 (+/-0.312) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.609 (+/-0.242) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.240) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.007) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.600 (+/-0.245) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.698 (+/-0.307) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.696 (+/-0.300) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.693 (+/-0.304) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.658 (+/-0.313) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.659 (+/-0.313) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.611 (+/-0.239) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.240) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.239) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.007) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.700 (+/-0.309) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.639 (+/-0.207) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.694 (+/-0.302) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.705 (+/-0.338) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.658 (+/-0.313) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.633 (+/-0.320) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.611 (+/-0.241) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.240) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.239) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.007) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.699 (+/-0.307) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.575 (+/-0.233) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.695 (+/-0.304) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.679 (+/-0.362) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.658 (+/-0.313) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.633 (+/-0.321) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.611 (+/-0.241) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.240) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.239) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.007) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.700 (+/-0.309) for {'C': 0.1, 'kernel': 'linear'}
0.698 (+/-0.305) for {'C': 1.0, 'kernel': 'linear'}
0.694 (+/-0.301) for {'C': 10.0, 'kernel': 'linear'}
0.657 (+/-0.313) for {'C': 100.0, 'kernel': 'linear'}
0.659 (+/-0.314) for {'C': 1000.0, 'kernel': 'linear'}
0.658 (+/-0.308) for {'C': 10000.0, 'kernel': 'linear'}
0.633 (+/-0.322) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.04 0.17 0.07 6
1 0.99 0.96 0.98 623
avg / total 0.98 0.96 0.97 629
本轮grid search结果,得到最好的参数选择是: {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.7051261439525104
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.660 (+/-0.219) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.634 (+/-0.193) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.630 (+/-0.189) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.611 (+/-0.184) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.622 (+/-0.185) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.606 (+/-0.165) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.616 (+/-0.179) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.616 (+/-0.182) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.602 (+/-0.196) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.602 (+/-0.196) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.597 (+/-0.195) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.664 (+/-0.291) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.655 (+/-0.286) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.625 (+/-0.184) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.597 (+/-0.161) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.619 (+/-0.170) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.610 (+/-0.168) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.614 (+/-0.179) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.598 (+/-0.191) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.598 (+/-0.192) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.592 (+/-0.192) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.592 (+/-0.194) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.653 (+/-0.286) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.636 (+/-0.288) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.595 (+/-0.160) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.601 (+/-0.157) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.615 (+/-0.172) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.622 (+/-0.189) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.597 (+/-0.189) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.599 (+/-0.192) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.595 (+/-0.192) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.591 (+/-0.193) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.592 (+/-0.195) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.645 (+/-0.286) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.625 (+/-0.289) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.595 (+/-0.160) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.597 (+/-0.155) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.618 (+/-0.172) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.610 (+/-0.180) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.592 (+/-0.188) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.596 (+/-0.193) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.595 (+/-0.194) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.590 (+/-0.194) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.591 (+/-0.194) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.623 (+/-0.288) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.621 (+/-0.290) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.608 (+/-0.186) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.601 (+/-0.159) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.591 (+/-0.173) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.588 (+/-0.170) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.589 (+/-0.188) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.595 (+/-0.192) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.592 (+/-0.193) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.590 (+/-0.194) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.590 (+/-0.194) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.620 (+/-0.289) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.623 (+/-0.289) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.614 (+/-0.182) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.586 (+/-0.184) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.598 (+/-0.194) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.586 (+/-0.170) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.591 (+/-0.193) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.594 (+/-0.194) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.589 (+/-0.194) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.590 (+/-0.194) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.588 (+/-0.194) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.618 (+/-0.290) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.597 (+/-0.175) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.616 (+/-0.183) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.586 (+/-0.184) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.592 (+/-0.177) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.588 (+/-0.170) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.591 (+/-0.193) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.593 (+/-0.193) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.587 (+/-0.195) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.591 (+/-0.196) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.588 (+/-0.194) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.617 (+/-0.290) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.594 (+/-0.175) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.622 (+/-0.212) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.585 (+/-0.184) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.591 (+/-0.177) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.588 (+/-0.170) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.583 (+/-0.171) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.594 (+/-0.195) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.587 (+/-0.195) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.591 (+/-0.195) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.588 (+/-0.194) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.617 (+/-0.290) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.593 (+/-0.175) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.630 (+/-0.224) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.585 (+/-0.184) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.588 (+/-0.174) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.587 (+/-0.170) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.582 (+/-0.172) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.593 (+/-0.195) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.587 (+/-0.195) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.591 (+/-0.195) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.588 (+/-0.194) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.618 (+/-0.289) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.593 (+/-0.175) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.625 (+/-0.215) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.587 (+/-0.185) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.588 (+/-0.175) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.587 (+/-0.170) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.582 (+/-0.172) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.591 (+/-0.194) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.588 (+/-0.194) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.591 (+/-0.195) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.588 (+/-0.194) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.623 (+/-0.289) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.599 (+/-0.176) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.625 (+/-0.215) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.586 (+/-0.185) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.588 (+/-0.175) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.587 (+/-0.170) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.582 (+/-0.172) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.591 (+/-0.194) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.588 (+/-0.194) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.589 (+/-0.195) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.588 (+/-0.194) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.690 (+/-0.300) for {'C': 1.0, 'kernel': 'linear'}
0.613 (+/-0.190) for {'C': 10.0, 'kernel': 'linear'}
0.608 (+/-0.197) for {'C': 100.0, 'kernel': 'linear'}
0.594 (+/-0.195) for {'C': 1000.0, 'kernel': 'linear'}
0.586 (+/-0.195) for {'C': 10000.0, 'kernel': 'linear'}
0.582 (+/-0.201) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.698 (+/-0.306) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.698 (+/-0.306) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.697 (+/-0.305) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.696 (+/-0.304) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.697 (+/-0.304) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.696 (+/-0.301) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.697 (+/-0.304) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.680 (+/-0.291) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.663 (+/-0.314) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.663 (+/-0.314) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.662 (+/-0.315) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.698 (+/-0.305) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.698 (+/-0.305) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.697 (+/-0.304) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.695 (+/-0.303) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.697 (+/-0.305) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.696 (+/-0.301) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.680 (+/-0.293) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.662 (+/-0.313) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.662 (+/-0.314) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.661 (+/-0.313) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.660 (+/-0.314) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.697 (+/-0.304) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.696 (+/-0.304) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.695 (+/-0.303) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.696 (+/-0.304) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.696 (+/-0.306) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.696 (+/-0.302) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.663 (+/-0.313) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.662 (+/-0.314) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.661 (+/-0.314) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.660 (+/-0.313) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.660 (+/-0.314) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.697 (+/-0.303) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.696 (+/-0.302) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.695 (+/-0.303) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.695 (+/-0.303) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.696 (+/-0.307) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.679 (+/-0.289) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.662 (+/-0.313) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.661 (+/-0.315) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.660 (+/-0.316) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.659 (+/-0.315) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.660 (+/-0.314) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.695 (+/-0.302) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.695 (+/-0.302) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.696 (+/-0.305) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.696 (+/-0.304) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.662 (+/-0.314) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.662 (+/-0.312) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.661 (+/-0.313) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.661 (+/-0.315) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.660 (+/-0.315) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.659 (+/-0.315) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.659 (+/-0.314) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.695 (+/-0.301) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.695 (+/-0.301) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.696 (+/-0.305) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.662 (+/-0.310) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.662 (+/-0.314) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.661 (+/-0.310) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.660 (+/-0.313) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.660 (+/-0.316) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.659 (+/-0.314) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.659 (+/-0.315) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.658 (+/-0.312) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.695 (+/-0.301) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.695 (+/-0.301) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.696 (+/-0.307) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.661 (+/-0.311) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.662 (+/-0.314) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.661 (+/-0.312) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.660 (+/-0.314) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.660 (+/-0.316) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.658 (+/-0.312) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.658 (+/-0.316) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.658 (+/-0.311) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.695 (+/-0.301) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.695 (+/-0.301) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.696 (+/-0.307) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.661 (+/-0.311) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.662 (+/-0.315) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.661 (+/-0.313) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.660 (+/-0.314) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.660 (+/-0.316) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.658 (+/-0.312) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.658 (+/-0.316) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.658 (+/-0.311) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.695 (+/-0.300) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.695 (+/-0.301) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.697 (+/-0.307) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.661 (+/-0.311) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.662 (+/-0.314) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.661 (+/-0.312) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.659 (+/-0.314) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.660 (+/-0.316) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.658 (+/-0.311) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.659 (+/-0.316) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.658 (+/-0.312) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.695 (+/-0.301) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.695 (+/-0.301) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.697 (+/-0.308) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.662 (+/-0.312) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.661 (+/-0.314) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.661 (+/-0.312) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.659 (+/-0.314) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.660 (+/-0.314) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.658 (+/-0.313) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.659 (+/-0.316) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.658 (+/-0.312) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.695 (+/-0.302) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.695 (+/-0.302) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.697 (+/-0.308) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.662 (+/-0.312) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.661 (+/-0.314) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.661 (+/-0.312) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.659 (+/-0.314) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.659 (+/-0.314) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.658 (+/-0.313) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.658 (+/-0.316) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.658 (+/-0.313) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.699 (+/-0.307) for {'C': 1.0, 'kernel': 'linear'}
0.696 (+/-0.304) for {'C': 10.0, 'kernel': 'linear'}
0.663 (+/-0.314) for {'C': 100.0, 'kernel': 'linear'}
0.660 (+/-0.315) for {'C': 1000.0, 'kernel': 'linear'}
0.658 (+/-0.307) for {'C': 10000.0, 'kernel': 'linear'}
0.633 (+/-0.322) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.20 0.17 0.18 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.99 0.99 629
本轮grid search结果,得到最好的参数选择是: {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6985561464259213
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.604 (+/-0.176) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.614 (+/-0.292) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.583 (+/-0.151) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.580 (+/-0.144) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.603 (+/-0.163) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.594 (+/-0.162) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.603 (+/-0.183) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.603 (+/-0.182) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.588 (+/-0.194) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.586 (+/-0.196) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.587 (+/-0.195) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.616 (+/-0.291) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.586 (+/-0.177) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.580 (+/-0.144) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.588 (+/-0.153) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.601 (+/-0.165) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.601 (+/-0.165) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.598 (+/-0.179) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.589 (+/-0.193) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.585 (+/-0.196) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.586 (+/-0.196) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.587 (+/-0.195) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.599 (+/-0.279) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.586 (+/-0.176) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.583 (+/-0.145) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.590 (+/-0.154) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.602 (+/-0.164) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.613 (+/-0.184) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.581 (+/-0.191) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.586 (+/-0.196) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.588 (+/-0.194) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.586 (+/-0.196) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.587 (+/-0.195) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.607 (+/-0.280) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.579 (+/-0.151) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.590 (+/-0.155) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.591 (+/-0.157) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.601 (+/-0.173) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.605 (+/-0.177) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.582 (+/-0.191) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.587 (+/-0.195) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.585 (+/-0.196) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.586 (+/-0.195) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.588 (+/-0.194) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.603 (+/-0.278) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.577 (+/-0.143) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.584 (+/-0.145) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.600 (+/-0.148) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.580 (+/-0.171) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.582 (+/-0.166) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.582 (+/-0.191) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.586 (+/-0.195) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.585 (+/-0.196) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.585 (+/-0.196) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.588 (+/-0.194) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.607 (+/-0.277) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.582 (+/-0.146) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.593 (+/-0.152) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.587 (+/-0.177) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.595 (+/-0.185) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.582 (+/-0.166) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.586 (+/-0.196) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.586 (+/-0.195) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.586 (+/-0.195) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.586 (+/-0.196) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.588 (+/-0.194) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.604 (+/-0.277) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.581 (+/-0.144) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.595 (+/-0.154) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.585 (+/-0.178) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.586 (+/-0.163) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.582 (+/-0.167) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.587 (+/-0.196) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.586 (+/-0.195) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.585 (+/-0.196) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.584 (+/-0.197) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.588 (+/-0.194) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.606 (+/-0.277) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.578 (+/-0.143) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.596 (+/-0.158) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.585 (+/-0.178) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.586 (+/-0.163) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.578 (+/-0.172) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.578 (+/-0.173) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.586 (+/-0.195) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.586 (+/-0.196) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.584 (+/-0.197) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.588 (+/-0.194) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.610 (+/-0.278) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.576 (+/-0.143) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.603 (+/-0.180) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.585 (+/-0.179) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.586 (+/-0.163) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.578 (+/-0.172) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.578 (+/-0.173) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.587 (+/-0.195) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.586 (+/-0.196) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.584 (+/-0.197) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.588 (+/-0.194) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.611 (+/-0.277) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.576 (+/-0.143) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.597 (+/-0.166) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.585 (+/-0.179) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.585 (+/-0.164) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.578 (+/-0.172) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.578 (+/-0.173) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.587 (+/-0.195) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.586 (+/-0.195) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.584 (+/-0.197) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.588 (+/-0.194) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.618 (+/-0.278) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.583 (+/-0.146) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.597 (+/-0.166) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.585 (+/-0.179) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.585 (+/-0.164) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.578 (+/-0.172) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.578 (+/-0.173) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.587 (+/-0.195) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.586 (+/-0.195) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.584 (+/-0.197) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.588 (+/-0.194) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.798 (+/-0.437) for {'C': 0.1, 'kernel': 'linear'}
0.635 (+/-0.198) for {'C': 1.0, 'kernel': 'linear'}
0.586 (+/-0.152) for {'C': 10.0, 'kernel': 'linear'}
0.587 (+/-0.195) for {'C': 100.0, 'kernel': 'linear'}
0.589 (+/-0.194) for {'C': 1000.0, 'kernel': 'linear'}
0.586 (+/-0.195) for {'C': 10000.0, 'kernel': 'linear'}
0.582 (+/-0.201) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.696 (+/-0.300) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.694 (+/-0.299) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.694 (+/-0.299) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.694 (+/-0.300) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.695 (+/-0.304) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.693 (+/-0.304) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.716 (+/-0.351) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.699 (+/-0.346) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.681 (+/-0.365) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.656 (+/-0.312) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.658 (+/-0.313) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.694 (+/-0.298) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.693 (+/-0.296) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.693 (+/-0.300) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.694 (+/-0.303) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.694 (+/-0.306) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.717 (+/-0.352) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.698 (+/-0.345) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.682 (+/-0.366) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.656 (+/-0.311) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.656 (+/-0.312) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.658 (+/-0.311) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.693 (+/-0.298) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.693 (+/-0.296) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.694 (+/-0.304) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.694 (+/-0.304) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.717 (+/-0.353) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.741 (+/-0.313) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.655 (+/-0.313) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.656 (+/-0.312) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.681 (+/-0.365) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.656 (+/-0.311) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.658 (+/-0.311) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.694 (+/-0.301) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.693 (+/-0.297) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.694 (+/-0.305) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.693 (+/-0.304) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.715 (+/-0.352) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.723 (+/-0.311) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.655 (+/-0.314) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.657 (+/-0.313) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.656 (+/-0.310) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.656 (+/-0.311) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.658 (+/-0.313) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.694 (+/-0.301) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.693 (+/-0.299) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.694 (+/-0.304) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.742 (+/-0.316) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.681 (+/-0.365) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.706 (+/-0.336) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.656 (+/-0.314) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.657 (+/-0.312) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.656 (+/-0.309) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.656 (+/-0.308) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.658 (+/-0.313) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.694 (+/-0.302) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.694 (+/-0.300) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.694 (+/-0.305) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.708 (+/-0.341) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.705 (+/-0.345) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.705 (+/-0.338) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.656 (+/-0.314) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.656 (+/-0.313) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.657 (+/-0.310) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.656 (+/-0.309) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.658 (+/-0.313) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.694 (+/-0.301) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.694 (+/-0.299) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.694 (+/-0.306) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.708 (+/-0.340) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.705 (+/-0.346) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.705 (+/-0.337) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.656 (+/-0.315) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.657 (+/-0.313) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.656 (+/-0.309) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.655 (+/-0.309) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.658 (+/-0.312) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.694 (+/-0.301) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.693 (+/-0.299) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.694 (+/-0.306) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.708 (+/-0.339) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.704 (+/-0.347) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.680 (+/-0.362) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.656 (+/-0.315) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.657 (+/-0.313) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.657 (+/-0.310) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.656 (+/-0.308) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.658 (+/-0.312) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.695 (+/-0.303) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.693 (+/-0.299) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.694 (+/-0.307) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.707 (+/-0.339) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.703 (+/-0.347) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.679 (+/-0.362) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.656 (+/-0.314) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.657 (+/-0.314) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.657 (+/-0.310) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.656 (+/-0.308) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.658 (+/-0.312) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.695 (+/-0.303) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.693 (+/-0.299) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.694 (+/-0.307) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.707 (+/-0.339) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.703 (+/-0.346) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.679 (+/-0.362) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.656 (+/-0.315) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.657 (+/-0.314) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.657 (+/-0.310) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.656 (+/-0.308) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.658 (+/-0.312) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.695 (+/-0.304) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.694 (+/-0.300) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.694 (+/-0.307) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.707 (+/-0.339) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.704 (+/-0.344) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.679 (+/-0.362) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.656 (+/-0.314) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.657 (+/-0.313) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.657 (+/-0.310) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.655 (+/-0.309) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.658 (+/-0.313) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.700 (+/-0.309) for {'C': 0.1, 'kernel': 'linear'}
0.698 (+/-0.305) for {'C': 1.0, 'kernel': 'linear'}
0.694 (+/-0.301) for {'C': 10.0, 'kernel': 'linear'}
0.657 (+/-0.313) for {'C': 100.0, 'kernel': 'linear'}
0.659 (+/-0.314) for {'C': 1000.0, 'kernel': 'linear'}
0.658 (+/-0.308) for {'C': 10000.0, 'kernel': 'linear'}
0.633 (+/-0.322) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.07 0.17 0.10 6
1 0.99 0.98 0.98 623
avg / total 0.98 0.97 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.7423051158380741
循环迭代之前,delta is: [3.69042656e+06 6.01892829e-06]
执行tunemodel()函数前,使用的grid search parameter是:
[{'C': array([3981071.70553497, 4365158.32240166, 4786300.92322638,
5248074.60249772, 5754399.37337156, 6309573.44480193,
6918309.70918936, 7585775.75029184, 8317637.7110267 ,
9120108.39355909, 9999999.99999999]), 'kernel': ['rbf'], 'gamma': array([2.51188643e-06, 2.75422870e-06, 3.01995172e-06, 3.31131121e-06,
3.63078055e-06, 3.98107171e-06, 4.36515832e-06, 4.78630092e-06,
5.24807460e-06, 5.75439937e-06, 6.30957344e-06])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.595 (+/-0.160) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.627 (+/-0.200) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.597 (+/-0.161) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.635 (+/-0.287) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.600 (+/-0.164) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.597 (+/-0.155) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.599 (+/-0.159) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.623 (+/-0.184) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.607 (+/-0.180) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.622 (+/-0.174) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.618 (+/-0.172) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.591 (+/-0.154) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.652 (+/-0.295) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.597 (+/-0.161) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.641 (+/-0.289) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.608 (+/-0.184) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.597 (+/-0.155) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.596 (+/-0.159) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.623 (+/-0.184) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.596 (+/-0.192) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.630 (+/-0.189) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.608 (+/-0.161) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.591 (+/-0.154) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.654 (+/-0.294) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.597 (+/-0.161) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.639 (+/-0.290) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.604 (+/-0.180) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.597 (+/-0.155) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.596 (+/-0.159) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.623 (+/-0.184) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.596 (+/-0.192) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.639 (+/-0.202) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.608 (+/-0.161) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.598 (+/-0.177) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.654 (+/-0.294) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.598 (+/-0.160) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.635 (+/-0.287) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.609 (+/-0.181) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.599 (+/-0.158) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.602 (+/-0.166) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.616 (+/-0.182) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.597 (+/-0.192) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.639 (+/-0.202) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.605 (+/-0.161) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.603 (+/-0.181) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.654 (+/-0.294) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.598 (+/-0.160) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.636 (+/-0.286) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.609 (+/-0.181) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.600 (+/-0.158) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.605 (+/-0.179) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.614 (+/-0.182) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.597 (+/-0.192) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.639 (+/-0.202) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.595 (+/-0.176) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.608 (+/-0.186) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.654 (+/-0.294) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.592 (+/-0.155) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.642 (+/-0.289) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.609 (+/-0.180) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.601 (+/-0.159) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.603 (+/-0.180) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.614 (+/-0.182) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.594 (+/-0.191) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.639 (+/-0.202) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.591 (+/-0.173) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.604 (+/-0.180) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.654 (+/-0.294) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.596 (+/-0.155) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.652 (+/-0.289) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.609 (+/-0.180) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.591 (+/-0.152) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.605 (+/-0.180) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.605 (+/-0.163) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.594 (+/-0.191) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.627 (+/-0.197) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.593 (+/-0.176) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.602 (+/-0.178) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.644 (+/-0.288) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.596 (+/-0.155) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.649 (+/-0.288) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.609 (+/-0.180) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.588 (+/-0.150) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.593 (+/-0.191) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.608 (+/-0.164) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.596 (+/-0.191) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.626 (+/-0.188) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.592 (+/-0.177) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.609 (+/-0.184) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.650 (+/-0.287) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.594 (+/-0.155) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.657 (+/-0.303) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.613 (+/-0.186) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.588 (+/-0.150) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.593 (+/-0.191) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.595 (+/-0.176) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.597 (+/-0.191) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.624 (+/-0.189) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.592 (+/-0.177) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.609 (+/-0.184) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.650 (+/-0.287) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.594 (+/-0.155) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.657 (+/-0.303) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.613 (+/-0.186) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.596 (+/-0.174) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.584 (+/-0.170) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.598 (+/-0.183) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.593 (+/-0.190) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.611 (+/-0.204) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.598 (+/-0.194) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.614 (+/-0.182) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.650 (+/-0.287) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.597 (+/-0.158) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.674 (+/-0.354) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.612 (+/-0.186) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.586 (+/-0.184) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.584 (+/-0.170) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.595 (+/-0.176) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.593 (+/-0.190) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.607 (+/-0.201) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.598 (+/-0.194) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.690 (+/-0.300) for {'C': 1.0, 'kernel': 'linear'}
0.613 (+/-0.190) for {'C': 10.0, 'kernel': 'linear'}
0.608 (+/-0.197) for {'C': 100.0, 'kernel': 'linear'}
0.594 (+/-0.195) for {'C': 1000.0, 'kernel': 'linear'}
0.586 (+/-0.195) for {'C': 10000.0, 'kernel': 'linear'}
0.582 (+/-0.201) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.695 (+/-0.303) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.697 (+/-0.307) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.695 (+/-0.300) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.696 (+/-0.303) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.695 (+/-0.300) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.695 (+/-0.303) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.695 (+/-0.301) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.697 (+/-0.305) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.679 (+/-0.287) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.697 (+/-0.307) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.696 (+/-0.307) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.695 (+/-0.303) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.697 (+/-0.308) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.695 (+/-0.300) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.696 (+/-0.305) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.695 (+/-0.300) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.695 (+/-0.303) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.695 (+/-0.301) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.697 (+/-0.305) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.662 (+/-0.309) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.697 (+/-0.307) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.679 (+/-0.294) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.695 (+/-0.303) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.697 (+/-0.308) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.695 (+/-0.300) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.696 (+/-0.305) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.695 (+/-0.300) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.695 (+/-0.303) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.695 (+/-0.301) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.697 (+/-0.305) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.662 (+/-0.309) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.697 (+/-0.308) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.679 (+/-0.294) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.695 (+/-0.303) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.697 (+/-0.308) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.695 (+/-0.301) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.696 (+/-0.304) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.695 (+/-0.300) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.695 (+/-0.303) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.695 (+/-0.302) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.680 (+/-0.293) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.662 (+/-0.309) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.697 (+/-0.308) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.679 (+/-0.293) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.695 (+/-0.304) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.697 (+/-0.308) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.695 (+/-0.301) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.696 (+/-0.304) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.695 (+/-0.300) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.696 (+/-0.304) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.679 (+/-0.289) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.680 (+/-0.293) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.662 (+/-0.309) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.697 (+/-0.308) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.663 (+/-0.314) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.696 (+/-0.305) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.697 (+/-0.308) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.695 (+/-0.300) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.696 (+/-0.304) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.695 (+/-0.301) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.696 (+/-0.304) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.679 (+/-0.288) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.680 (+/-0.294) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.662 (+/-0.310) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.697 (+/-0.308) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.662 (+/-0.314) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.695 (+/-0.304) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.697 (+/-0.308) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.695 (+/-0.302) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.697 (+/-0.306) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.695 (+/-0.301) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.679 (+/-0.290) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.679 (+/-0.289) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.679 (+/-0.294) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.662 (+/-0.309) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.680 (+/-0.296) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.663 (+/-0.314) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.695 (+/-0.303) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.697 (+/-0.308) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.695 (+/-0.302) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.697 (+/-0.307) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.695 (+/-0.301) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.678 (+/-0.289) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.662 (+/-0.309) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.679 (+/-0.294) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.662 (+/-0.311) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.680 (+/-0.296) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.662 (+/-0.314) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.696 (+/-0.304) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.697 (+/-0.308) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.695 (+/-0.300) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.697 (+/-0.308) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.695 (+/-0.301) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.678 (+/-0.289) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.662 (+/-0.309) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.663 (+/-0.314) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.663 (+/-0.311) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.680 (+/-0.295) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.662 (+/-0.314) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.696 (+/-0.305) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.697 (+/-0.308) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.695 (+/-0.300) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.697 (+/-0.308) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.695 (+/-0.301) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.678 (+/-0.289) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.662 (+/-0.309) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.663 (+/-0.315) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.662 (+/-0.310) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.663 (+/-0.316) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.662 (+/-0.314) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.696 (+/-0.305) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.697 (+/-0.308) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.695 (+/-0.301) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.697 (+/-0.309) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.695 (+/-0.301) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.662 (+/-0.310) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.662 (+/-0.309) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.663 (+/-0.314) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.662 (+/-0.310) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.663 (+/-0.316) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.662 (+/-0.314) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.699 (+/-0.307) for {'C': 1.0, 'kernel': 'linear'}
0.696 (+/-0.304) for {'C': 10.0, 'kernel': 'linear'}
0.663 (+/-0.314) for {'C': 100.0, 'kernel': 'linear'}
0.660 (+/-0.315) for {'C': 1000.0, 'kernel': 'linear'}
0.658 (+/-0.307) for {'C': 10000.0, 'kernel': 'linear'}
0.633 (+/-0.322) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.20 0.17 0.18 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.99 0.99 629
本轮grid search结果,得到最好的参数选择是: {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6985561464259213
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.590 (+/-0.155) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.610 (+/-0.170) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.589 (+/-0.154) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.625 (+/-0.278) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.588 (+/-0.160) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.591 (+/-0.157) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.591 (+/-0.161) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.608 (+/-0.186) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.602 (+/-0.180) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.601 (+/-0.165) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.601 (+/-0.173) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.586 (+/-0.149) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.610 (+/-0.170) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.589 (+/-0.154) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.628 (+/-0.280) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.596 (+/-0.183) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.595 (+/-0.155) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.588 (+/-0.160) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.608 (+/-0.186) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.591 (+/-0.192) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.608 (+/-0.185) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.594 (+/-0.162) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.586 (+/-0.149) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.608 (+/-0.164) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.589 (+/-0.154) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.627 (+/-0.281) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.595 (+/-0.183) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.595 (+/-0.155) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.588 (+/-0.160) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.607 (+/-0.187) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.591 (+/-0.192) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.611 (+/-0.190) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.593 (+/-0.162) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.582 (+/-0.144) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.608 (+/-0.164) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.588 (+/-0.155) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.625 (+/-0.281) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.600 (+/-0.185) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.596 (+/-0.159) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.594 (+/-0.169) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.600 (+/-0.183) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.590 (+/-0.192) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.611 (+/-0.191) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.593 (+/-0.162) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.583 (+/-0.146) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.608 (+/-0.164) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.588 (+/-0.155) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.629 (+/-0.281) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.599 (+/-0.186) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.601 (+/-0.148) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.598 (+/-0.184) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.597 (+/-0.182) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.590 (+/-0.193) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.611 (+/-0.191) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.580 (+/-0.171) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.584 (+/-0.145) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.608 (+/-0.164) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.587 (+/-0.154) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.635 (+/-0.284) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.599 (+/-0.186) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.600 (+/-0.148) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.598 (+/-0.184) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.596 (+/-0.183) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.590 (+/-0.192) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.610 (+/-0.191) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.580 (+/-0.171) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.584 (+/-0.145) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.608 (+/-0.164) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.588 (+/-0.155) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.635 (+/-0.284) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.599 (+/-0.186) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.593 (+/-0.142) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.601 (+/-0.184) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.588 (+/-0.161) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.590 (+/-0.193) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.602 (+/-0.183) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.580 (+/-0.171) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.584 (+/-0.145) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.606 (+/-0.164) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.588 (+/-0.155) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.635 (+/-0.284) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.599 (+/-0.186) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.590 (+/-0.141) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.588 (+/-0.194) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.590 (+/-0.162) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.589 (+/-0.193) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.602 (+/-0.183) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.580 (+/-0.171) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.589 (+/-0.152) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.612 (+/-0.167) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.588 (+/-0.155) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.634 (+/-0.285) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.602 (+/-0.192) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.589 (+/-0.142) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.588 (+/-0.194) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.577 (+/-0.170) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.589 (+/-0.193) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.599 (+/-0.183) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.580 (+/-0.171) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.589 (+/-0.152) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.612 (+/-0.167) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.588 (+/-0.155) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.634 (+/-0.285) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.602 (+/-0.192) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.597 (+/-0.166) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.583 (+/-0.171) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.577 (+/-0.170) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.587 (+/-0.192) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.590 (+/-0.187) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.588 (+/-0.194) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.593 (+/-0.152) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.612 (+/-0.167) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.590 (+/-0.159) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.634 (+/-0.285) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.602 (+/-0.192) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.587 (+/-0.177) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.583 (+/-0.171) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.576 (+/-0.170) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.587 (+/-0.192) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.590 (+/-0.187) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.595 (+/-0.185) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.798 (+/-0.437) for {'C': 0.1, 'kernel': 'linear'}
0.635 (+/-0.198) for {'C': 1.0, 'kernel': 'linear'}
0.586 (+/-0.152) for {'C': 10.0, 'kernel': 'linear'}
0.587 (+/-0.195) for {'C': 100.0, 'kernel': 'linear'}
0.589 (+/-0.194) for {'C': 1000.0, 'kernel': 'linear'}
0.586 (+/-0.195) for {'C': 10000.0, 'kernel': 'linear'}
0.582 (+/-0.201) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.694 (+/-0.305) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.696 (+/-0.307) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.694 (+/-0.300) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.695 (+/-0.306) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.692 (+/-0.299) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.693 (+/-0.304) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.692 (+/-0.304) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.693 (+/-0.306) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.701 (+/-0.346) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.716 (+/-0.353) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.715 (+/-0.352) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.694 (+/-0.305) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.696 (+/-0.307) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.694 (+/-0.300) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.695 (+/-0.307) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.692 (+/-0.299) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.718 (+/-0.352) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.692 (+/-0.305) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.693 (+/-0.306) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.684 (+/-0.366) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.716 (+/-0.353) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.699 (+/-0.345) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.694 (+/-0.305) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.696 (+/-0.307) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.694 (+/-0.300) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.695 (+/-0.306) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.692 (+/-0.298) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.718 (+/-0.352) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.692 (+/-0.305) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.693 (+/-0.306) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.683 (+/-0.366) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.716 (+/-0.353) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.699 (+/-0.344) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.693 (+/-0.304) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.696 (+/-0.307) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.694 (+/-0.300) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.695 (+/-0.307) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.692 (+/-0.298) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.718 (+/-0.351) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.692 (+/-0.305) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.676 (+/-0.293) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.683 (+/-0.366) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.716 (+/-0.353) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.698 (+/-0.344) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.694 (+/-0.304) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.696 (+/-0.307) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.693 (+/-0.300) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.695 (+/-0.307) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.692 (+/-0.298) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.743 (+/-0.316) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.675 (+/-0.292) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.676 (+/-0.292) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.683 (+/-0.365) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.716 (+/-0.354) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.681 (+/-0.365) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.694 (+/-0.304) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.696 (+/-0.307) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.693 (+/-0.301) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.695 (+/-0.309) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.692 (+/-0.298) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.742 (+/-0.316) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.675 (+/-0.292) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.676 (+/-0.292) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.683 (+/-0.366) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.716 (+/-0.353) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.681 (+/-0.365) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.694 (+/-0.304) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.696 (+/-0.307) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.693 (+/-0.302) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.695 (+/-0.309) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.692 (+/-0.298) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.726 (+/-0.314) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.675 (+/-0.292) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.675 (+/-0.292) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.683 (+/-0.365) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.699 (+/-0.345) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.681 (+/-0.365) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.694 (+/-0.304) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.696 (+/-0.307) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.693 (+/-0.302) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.695 (+/-0.309) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.691 (+/-0.299) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.725 (+/-0.313) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.658 (+/-0.311) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.676 (+/-0.292) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.683 (+/-0.365) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.699 (+/-0.345) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.681 (+/-0.366) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.694 (+/-0.304) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.696 (+/-0.307) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.693 (+/-0.302) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.695 (+/-0.309) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.691 (+/-0.299) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.725 (+/-0.313) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.658 (+/-0.312) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.659 (+/-0.311) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.682 (+/-0.365) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.698 (+/-0.345) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.681 (+/-0.366) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.694 (+/-0.304) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.696 (+/-0.307) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.693 (+/-0.302) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.695 (+/-0.310) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.691 (+/-0.299) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.725 (+/-0.313) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.683 (+/-0.367) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.658 (+/-0.312) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.682 (+/-0.366) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.707 (+/-0.339) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.680 (+/-0.366) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.694 (+/-0.305) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.696 (+/-0.307) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.693 (+/-0.302) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.694 (+/-0.311) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.691 (+/-0.299) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.708 (+/-0.341) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.682 (+/-0.367) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.658 (+/-0.311) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.682 (+/-0.366) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.707 (+/-0.339) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.705 (+/-0.345) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.700 (+/-0.309) for {'C': 0.1, 'kernel': 'linear'}
0.698 (+/-0.305) for {'C': 1.0, 'kernel': 'linear'}
0.694 (+/-0.301) for {'C': 10.0, 'kernel': 'linear'}
0.657 (+/-0.313) for {'C': 100.0, 'kernel': 'linear'}
0.659 (+/-0.314) for {'C': 1000.0, 'kernel': 'linear'}
0.658 (+/-0.308) for {'C': 10000.0, 'kernel': 'linear'}
0.633 (+/-0.322) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.07 0.17 0.10 6
1 0.99 0.98 0.98 623
avg / total 0.98 0.97 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.7426256286585868
第0轮loop中,tunemodel函数得到的最优参数是: ([{'C': array([5248074.60249772, 5345643.5939697 , 5445026.5284242 ,
5546257.1295791 , 5649369.74812302, 5754399.37337156,
5861381.64514028, 5970352.86583836, 6081350.01278718,
6194410.75076781, 6309573.44480193]), 'kernel': ['rbf'], 'gamma': array([3.63078055e-06, 3.69828180e-06, 3.76703799e-06, 3.83707245e-06,
3.90840896e-06, 3.98107171e-06, 4.05508535e-06, 4.13047502e-06,
4.20726628e-06, 4.28548520e-06, 4.36515832e-06])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}], {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}, 0.7426256286585868)
这是第0次迭代微调C和gamma。
第0次迭代,得到delta: [555174.07143037 0. ]
执行tunemodel()函数前,使用的grid search parameter是:
[{'C': array([5248074.60249772, 5345643.5939697 , 5445026.5284242 ,
5546257.1295791 , 5649369.74812302, 5754399.37337156,
5861381.64514028, 5970352.86583836, 6081350.01278718,
6194410.75076781, 6309573.44480193]), 'kernel': ['rbf'], 'gamma': array([3.63078055e-06, 3.69828180e-06, 3.76703799e-06, 3.83707245e-06,
3.90840896e-06, 3.98107171e-06, 4.05508535e-06, 4.13047502e-06,
4.20726628e-06, 4.28548520e-06, 4.36515832e-06])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.609 (+/-0.181) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.597 (+/-0.161) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.606 (+/-0.164) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.599 (+/-0.158) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.604 (+/-0.163) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.599 (+/-0.158) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.603 (+/-0.160) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.621 (+/-0.189) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.610 (+/-0.165) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.629 (+/-0.213) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.602 (+/-0.166) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.609 (+/-0.181) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.597 (+/-0.161) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.606 (+/-0.164) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.601 (+/-0.159) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.604 (+/-0.163) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.600 (+/-0.158) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.603 (+/-0.160) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.621 (+/-0.189) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.610 (+/-0.165) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.629 (+/-0.213) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.602 (+/-0.166) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.609 (+/-0.181) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.608 (+/-0.182) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.606 (+/-0.164) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.600 (+/-0.159) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.604 (+/-0.163) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.600 (+/-0.158) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.595 (+/-0.176) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.614 (+/-0.182) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.615 (+/-0.167) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.637 (+/-0.224) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.611 (+/-0.185) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.609 (+/-0.181) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.608 (+/-0.182) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.606 (+/-0.164) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.600 (+/-0.159) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.604 (+/-0.163) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.600 (+/-0.158) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.595 (+/-0.176) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.614 (+/-0.182) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.612 (+/-0.165) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.637 (+/-0.224) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.608 (+/-0.185) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.609 (+/-0.181) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.605 (+/-0.182) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.606 (+/-0.164) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.597 (+/-0.158) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.603 (+/-0.163) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.600 (+/-0.158) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.595 (+/-0.176) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.614 (+/-0.182) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.615 (+/-0.167) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.637 (+/-0.224) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.605 (+/-0.179) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.609 (+/-0.181) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.608 (+/-0.182) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.606 (+/-0.164) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.597 (+/-0.158) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.605 (+/-0.163) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.600 (+/-0.158) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.595 (+/-0.176) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.614 (+/-0.182) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.615 (+/-0.167) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.629 (+/-0.199) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.605 (+/-0.179) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.609 (+/-0.181) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.605 (+/-0.181) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.606 (+/-0.164) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.595 (+/-0.157) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.607 (+/-0.163) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.601 (+/-0.159) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.591 (+/-0.171) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.614 (+/-0.182) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.615 (+/-0.167) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.637 (+/-0.224) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.603 (+/-0.180) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.609 (+/-0.180) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.608 (+/-0.182) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.606 (+/-0.164) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.595 (+/-0.157) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.607 (+/-0.163) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.601 (+/-0.159) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.591 (+/-0.171) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.614 (+/-0.182) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.615 (+/-0.167) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.637 (+/-0.224) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.605 (+/-0.180) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.609 (+/-0.180) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.608 (+/-0.182) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.606 (+/-0.164) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.595 (+/-0.157) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.607 (+/-0.163) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.601 (+/-0.159) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.591 (+/-0.171) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.622 (+/-0.198) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.615 (+/-0.167) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.637 (+/-0.224) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.605 (+/-0.180) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.609 (+/-0.180) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.608 (+/-0.182) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.606 (+/-0.164) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.595 (+/-0.157) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.607 (+/-0.163) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.601 (+/-0.159) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.601 (+/-0.159) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.622 (+/-0.198) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.615 (+/-0.167) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.630 (+/-0.224) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.605 (+/-0.180) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.609 (+/-0.180) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.608 (+/-0.182) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.606 (+/-0.164) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.595 (+/-0.157) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.607 (+/-0.163) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.601 (+/-0.159) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.603 (+/-0.160) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.622 (+/-0.198) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.615 (+/-0.167) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.630 (+/-0.224) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.603 (+/-0.180) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.690 (+/-0.300) for {'C': 1.0, 'kernel': 'linear'}
0.613 (+/-0.190) for {'C': 10.0, 'kernel': 'linear'}
0.608 (+/-0.197) for {'C': 100.0, 'kernel': 'linear'}
0.594 (+/-0.195) for {'C': 1000.0, 'kernel': 'linear'}
0.586 (+/-0.195) for {'C': 10000.0, 'kernel': 'linear'}
0.582 (+/-0.201) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.695 (+/-0.300) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.694 (+/-0.298) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.696 (+/-0.301) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.696 (+/-0.303) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.696 (+/-0.303) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.695 (+/-0.303) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.679 (+/-0.293) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.697 (+/-0.307) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.696 (+/-0.305) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.697 (+/-0.308) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.695 (+/-0.302) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.695 (+/-0.300) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.694 (+/-0.298) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.696 (+/-0.301) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.696 (+/-0.303) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.696 (+/-0.303) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.696 (+/-0.304) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.679 (+/-0.293) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.697 (+/-0.307) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.696 (+/-0.305) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.697 (+/-0.308) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.695 (+/-0.302) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.695 (+/-0.300) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.695 (+/-0.298) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.696 (+/-0.301) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.695 (+/-0.301) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.696 (+/-0.303) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.696 (+/-0.304) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.663 (+/-0.314) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.680 (+/-0.295) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.696 (+/-0.306) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.697 (+/-0.308) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.695 (+/-0.302) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.695 (+/-0.300) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.695 (+/-0.298) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.696 (+/-0.301) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.695 (+/-0.301) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.696 (+/-0.303) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.696 (+/-0.304) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.663 (+/-0.314) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.680 (+/-0.295) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.696 (+/-0.306) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.697 (+/-0.308) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.695 (+/-0.302) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.695 (+/-0.300) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.695 (+/-0.298) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.696 (+/-0.301) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.695 (+/-0.301) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.696 (+/-0.302) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.696 (+/-0.304) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.663 (+/-0.314) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.680 (+/-0.295) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.696 (+/-0.306) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.697 (+/-0.308) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.679 (+/-0.289) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.695 (+/-0.300) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.695 (+/-0.298) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.696 (+/-0.301) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.695 (+/-0.301) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.696 (+/-0.303) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.696 (+/-0.304) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.663 (+/-0.314) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.680 (+/-0.295) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.696 (+/-0.306) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.697 (+/-0.307) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.679 (+/-0.289) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.695 (+/-0.300) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.695 (+/-0.299) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.696 (+/-0.301) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.695 (+/-0.301) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.696 (+/-0.303) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.696 (+/-0.304) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.663 (+/-0.313) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.680 (+/-0.295) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.696 (+/-0.306) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.697 (+/-0.308) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.679 (+/-0.288) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.695 (+/-0.301) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.695 (+/-0.298) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.696 (+/-0.301) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.695 (+/-0.301) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.696 (+/-0.303) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.696 (+/-0.304) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.663 (+/-0.313) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.680 (+/-0.295) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.696 (+/-0.306) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.697 (+/-0.308) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.679 (+/-0.289) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.695 (+/-0.301) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.695 (+/-0.299) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.696 (+/-0.301) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.695 (+/-0.301) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.696 (+/-0.303) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.696 (+/-0.304) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.663 (+/-0.313) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.680 (+/-0.295) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.696 (+/-0.306) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.697 (+/-0.308) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.679 (+/-0.289) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.695 (+/-0.301) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.695 (+/-0.298) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.696 (+/-0.301) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.695 (+/-0.301) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.696 (+/-0.303) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.696 (+/-0.304) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.679 (+/-0.292) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.680 (+/-0.295) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.696 (+/-0.306) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.680 (+/-0.296) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.679 (+/-0.289) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.695 (+/-0.301) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.695 (+/-0.298) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.696 (+/-0.301) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.695 (+/-0.301) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.696 (+/-0.303) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.696 (+/-0.304) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.679 (+/-0.293) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.680 (+/-0.295) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.696 (+/-0.306) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.680 (+/-0.296) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.679 (+/-0.288) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.699 (+/-0.307) for {'C': 1.0, 'kernel': 'linear'}
0.696 (+/-0.304) for {'C': 10.0, 'kernel': 'linear'}
0.663 (+/-0.314) for {'C': 100.0, 'kernel': 'linear'}
0.660 (+/-0.315) for {'C': 1000.0, 'kernel': 'linear'}
0.658 (+/-0.307) for {'C': 10000.0, 'kernel': 'linear'}
0.633 (+/-0.322) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.20 0.17 0.18 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.99 0.99 629
本轮grid search结果,得到最好的参数选择是: {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6985561464259213
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.600 (+/-0.185) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.591 (+/-0.161) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.598 (+/-0.167) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.597 (+/-0.159) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.599 (+/-0.164) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.596 (+/-0.159) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.591 (+/-0.161) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.600 (+/-0.172) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.595 (+/-0.161) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.600 (+/-0.165) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.594 (+/-0.169) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.600 (+/-0.185) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.591 (+/-0.161) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.598 (+/-0.167) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.597 (+/-0.159) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.599 (+/-0.164) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.596 (+/-0.159) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.591 (+/-0.161) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.599 (+/-0.172) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.595 (+/-0.161) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.600 (+/-0.165) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.594 (+/-0.169) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.600 (+/-0.185) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.601 (+/-0.184) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.598 (+/-0.167) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.597 (+/-0.159) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.599 (+/-0.164) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.595 (+/-0.159) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.581 (+/-0.171) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.591 (+/-0.160) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.600 (+/-0.165) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.607 (+/-0.185) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.602 (+/-0.190) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.600 (+/-0.185) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.602 (+/-0.183) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.598 (+/-0.167) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.597 (+/-0.159) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.599 (+/-0.164) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.596 (+/-0.159) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.581 (+/-0.171) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.591 (+/-0.160) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.597 (+/-0.163) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.607 (+/-0.185) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.602 (+/-0.190) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.600 (+/-0.185) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.599 (+/-0.183) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.598 (+/-0.167) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.596 (+/-0.159) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.599 (+/-0.164) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.601 (+/-0.148) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.581 (+/-0.171) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.591 (+/-0.160) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.599 (+/-0.166) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.607 (+/-0.185) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.599 (+/-0.183) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.599 (+/-0.186) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.601 (+/-0.184) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.598 (+/-0.167) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.596 (+/-0.159) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.599 (+/-0.164) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.601 (+/-0.148) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.581 (+/-0.171) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.591 (+/-0.160) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.599 (+/-0.166) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.606 (+/-0.185) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.598 (+/-0.184) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.599 (+/-0.186) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.599 (+/-0.183) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.598 (+/-0.167) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.594 (+/-0.158) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.599 (+/-0.164) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.601 (+/-0.148) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.581 (+/-0.171) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.591 (+/-0.160) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.599 (+/-0.166) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.606 (+/-0.185) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.599 (+/-0.183) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.599 (+/-0.186) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.602 (+/-0.183) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.598 (+/-0.167) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.593 (+/-0.158) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.599 (+/-0.164) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.601 (+/-0.148) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.581 (+/-0.171) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.591 (+/-0.160) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.599 (+/-0.166) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.606 (+/-0.185) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.601 (+/-0.184) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.599 (+/-0.186) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.602 (+/-0.183) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.598 (+/-0.167) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.593 (+/-0.158) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.598 (+/-0.164) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.600 (+/-0.148) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.581 (+/-0.171) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.599 (+/-0.182) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.599 (+/-0.166) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.606 (+/-0.185) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.601 (+/-0.184) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.599 (+/-0.186) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.602 (+/-0.183) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.597 (+/-0.168) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.593 (+/-0.158) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.598 (+/-0.164) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.600 (+/-0.148) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.591 (+/-0.161) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.599 (+/-0.182) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.599 (+/-0.166) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.599 (+/-0.181) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.601 (+/-0.184) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.599 (+/-0.186) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.602 (+/-0.183) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.596 (+/-0.168) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.593 (+/-0.158) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.598 (+/-0.164) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.600 (+/-0.148) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.591 (+/-0.161) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.599 (+/-0.183) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.599 (+/-0.166) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.599 (+/-0.181) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.598 (+/-0.184) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.798 (+/-0.437) for {'C': 0.1, 'kernel': 'linear'}
0.635 (+/-0.198) for {'C': 1.0, 'kernel': 'linear'}
0.586 (+/-0.152) for {'C': 10.0, 'kernel': 'linear'}
0.587 (+/-0.195) for {'C': 100.0, 'kernel': 'linear'}
0.589 (+/-0.194) for {'C': 1000.0, 'kernel': 'linear'}
0.586 (+/-0.195) for {'C': 10000.0, 'kernel': 'linear'}
0.582 (+/-0.201) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.692 (+/-0.298) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.692 (+/-0.301) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.693 (+/-0.302) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.718 (+/-0.351) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.718 (+/-0.350) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.718 (+/-0.351) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.676 (+/-0.291) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.693 (+/-0.304) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.694 (+/-0.303) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.694 (+/-0.306) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.692 (+/-0.305) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.692 (+/-0.298) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.692 (+/-0.301) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.693 (+/-0.302) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.718 (+/-0.351) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.718 (+/-0.350) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.718 (+/-0.351) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.676 (+/-0.291) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.693 (+/-0.303) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.694 (+/-0.303) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.694 (+/-0.306) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.692 (+/-0.306) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.692 (+/-0.298) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.693 (+/-0.301) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.693 (+/-0.303) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.718 (+/-0.351) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.718 (+/-0.351) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.718 (+/-0.351) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.659 (+/-0.311) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.676 (+/-0.290) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.694 (+/-0.304) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.694 (+/-0.306) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.692 (+/-0.306) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.692 (+/-0.298) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.693 (+/-0.302) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.693 (+/-0.303) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.718 (+/-0.351) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.718 (+/-0.351) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.718 (+/-0.351) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.659 (+/-0.311) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.676 (+/-0.290) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.694 (+/-0.303) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.694 (+/-0.306) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.692 (+/-0.306) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.692 (+/-0.298) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.693 (+/-0.302) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.693 (+/-0.303) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.718 (+/-0.351) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.718 (+/-0.351) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.743 (+/-0.316) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.659 (+/-0.311) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.676 (+/-0.290) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.694 (+/-0.304) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.694 (+/-0.306) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.675 (+/-0.292) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.692 (+/-0.298) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.693 (+/-0.300) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.693 (+/-0.303) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.718 (+/-0.351) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.718 (+/-0.351) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.743 (+/-0.316) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.659 (+/-0.311) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.676 (+/-0.290) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.694 (+/-0.304) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.694 (+/-0.305) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.675 (+/-0.292) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.692 (+/-0.298) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.692 (+/-0.300) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.693 (+/-0.303) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.718 (+/-0.351) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.718 (+/-0.351) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.743 (+/-0.316) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.659 (+/-0.311) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.676 (+/-0.290) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.694 (+/-0.304) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.694 (+/-0.305) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.675 (+/-0.292) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.692 (+/-0.298) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.693 (+/-0.302) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.693 (+/-0.303) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.718 (+/-0.350) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.718 (+/-0.351) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.743 (+/-0.316) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.659 (+/-0.311) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.676 (+/-0.290) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.694 (+/-0.304) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.694 (+/-0.305) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.675 (+/-0.292) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.692 (+/-0.298) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.693 (+/-0.302) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.693 (+/-0.303) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.718 (+/-0.350) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.718 (+/-0.350) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.742 (+/-0.315) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.659 (+/-0.311) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.676 (+/-0.290) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.694 (+/-0.304) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.694 (+/-0.305) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.675 (+/-0.292) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.692 (+/-0.298) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.693 (+/-0.302) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.693 (+/-0.302) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.718 (+/-0.350) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.718 (+/-0.350) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.742 (+/-0.316) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.676 (+/-0.292) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.676 (+/-0.290) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.694 (+/-0.304) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.677 (+/-0.292) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.675 (+/-0.292) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.692 (+/-0.298) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.693 (+/-0.302) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.693 (+/-0.301) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.718 (+/-0.350) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.718 (+/-0.350) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.742 (+/-0.316) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.675 (+/-0.292) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.676 (+/-0.289) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.694 (+/-0.304) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.677 (+/-0.292) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.675 (+/-0.292) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.700 (+/-0.309) for {'C': 0.1, 'kernel': 'linear'}
0.698 (+/-0.305) for {'C': 1.0, 'kernel': 'linear'}
0.694 (+/-0.301) for {'C': 10.0, 'kernel': 'linear'}
0.657 (+/-0.313) for {'C': 100.0, 'kernel': 'linear'}
0.659 (+/-0.314) for {'C': 1000.0, 'kernel': 'linear'}
0.658 (+/-0.308) for {'C': 10000.0, 'kernel': 'linear'}
0.633 (+/-0.322) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.07 0.17 0.10 6
1 0.99 0.98 0.98 623
avg / total 0.98 0.97 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.7427858850688432
第1轮loop中,tunemodel函数得到的最优参数是: ([{'C': array([5546257.1295791 , 5566727.98150948, 5587274.38994019,
5607896.6337451 , 5628594.99282729, 5649369.74812302,
5670221.18160543, 5691149.5762884 , 5712155.21623043,
5733238.38653839, 5754399.37337156]), 'kernel': ['rbf'], 'gamma': array([3.90840896e-06, 3.92283463e-06, 3.93731354e-06, 3.95184589e-06,
3.96643188e-06, 3.98107171e-06, 3.99576557e-06, 4.01051366e-06,
4.02531619e-06, 4.04017335e-06, 4.05508535e-06])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}], {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}, 0.7427858850688432)
这是第1次迭代微调C和gamma。
第1次迭代,得到delta: [105029.62524854 0. ]
SVC模型clf_op_iter的参数是: {'kernel': 'rbf', 'decision_function_shape': 'ovr', 'class_weight': {-1: 15}, 'tol': 0.001, 'gamma': 3.981071705534978e-06, 'random_state': 0, 'cache_size': 200, 'verbose': False, 'degree': 3, 'C': 5649369.748123019, 'probability': False, 'shrinking': True, 'coef0': 0.0, 'max_iter': -1}
训练模型SVC对训练样本的预测准确率: 0.9440113394755493
测试集中,预测为舞弊样本的有: (array([ 0, 1, 2, ..., 1254, 1255, 1256], dtype=int64),)
测试集中,实际为舞弊样本的有: (array([1246, 1247, 1248, 1249, 1250, 1251, 1252, 1253, 1254, 1255, 1256],
dtype=int64),)
预测的舞弊样本数目: 1055
训练模型SVC对测试样本的预测准确率: 0.24875974486180014
以上是第39次特征筛选。
第39次特征筛选,AUC值是: 0.5352035604844594
X_train_iter_svc.shape is: (1257, 13)
X_test_iter_svc.shape is: (1257, 13)
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.722 (+/-0.473) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.638 (+/-0.382) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.646 (+/-0.204) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.613 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.698 (+/-0.306) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.17 0.17 0.17 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6979146054909721
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.635 (+/-0.198) for {'C': 1.0, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.589 (+/-0.193) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.14 0.17 0.15 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.698 (+/-0.305) for {'C': 1.0, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.659 (+/-0.314) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.14 0.17 0.15 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.697594607964383
RBF的粗grid search最佳超参: {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001} RBF的粗grid search最高分值: 0.6979146054909721
linear的粗grid search最佳超参: {'C': 1.0, 'kernel': 'linear'} linear的粗grid search最高分值: 0.697594607964383
粗grid search得到的parameter是:
[{'C': array([1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02, 1.e+03, 1.e+04, 1.e+05,
1.e+06, 1.e+07, 1.e+08]), 'kernel': ['rbf'], 'gamma': array([1.e-08, 1.e-07, 1.e-06, 1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01,
1.e+00, 1.e+01, 1.e+02])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.848 (+/-0.459) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.848 (+/-0.459) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.665 (+/-0.225) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.618 (+/-0.309) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.848 (+/-0.459) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.646 (+/-0.204) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.660 (+/-0.290) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.636 (+/-0.384) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.848 (+/-0.459) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.646 (+/-0.204) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.167) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.601 (+/-0.315) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.638 (+/-0.382) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.848 (+/-0.459) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.646 (+/-0.204) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.167) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.595 (+/-0.192) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.608 (+/-0.309) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.638 (+/-0.382) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.848 (+/-0.459) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.646 (+/-0.204) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.615 (+/-0.167) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.592 (+/-0.188) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.576 (+/-0.207) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.610 (+/-0.308) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.638 (+/-0.382) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.731 (+/-0.365) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.660 (+/-0.219) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.622 (+/-0.184) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.596 (+/-0.193) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.593 (+/-0.195) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.581 (+/-0.201) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.610 (+/-0.308) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.638 (+/-0.382) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.848 (+/-0.460) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.707 (+/-0.416) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.620 (+/-0.290) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.594 (+/-0.191) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.588 (+/-0.194) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.584 (+/-0.200) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.583 (+/-0.200) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.610 (+/-0.308) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.638 (+/-0.382) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.793 (+/-0.440) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.671 (+/-0.375) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.623 (+/-0.289) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.593 (+/-0.191) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.587 (+/-0.195) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.584 (+/-0.200) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.583 (+/-0.200) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.610 (+/-0.308) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.638 (+/-0.382) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.690 (+/-0.300) for {'C': 1.0, 'kernel': 'linear'}
0.613 (+/-0.190) for {'C': 10.0, 'kernel': 'linear'}
0.608 (+/-0.197) for {'C': 100.0, 'kernel': 'linear'}
0.594 (+/-0.194) for {'C': 1000.0, 'kernel': 'linear'}
0.582 (+/-0.201) for {'C': 10000.0, 'kernel': 'linear'}
0.582 (+/-0.201) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [1]
检查交叉验证集中测试集标签是否有预测不到的值: {-1}
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 1.00 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.658 (+/-0.217) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.658 (+/-0.217) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.698 (+/-0.307) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.236) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.497 (+/-0.008) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.658 (+/-0.217) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.306) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.698 (+/-0.306) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.611 (+/-0.241) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.007) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.658 (+/-0.217) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.698 (+/-0.306) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.305) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.587 (+/-0.232) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.007) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.658 (+/-0.217) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.698 (+/-0.306) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.305) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.662 (+/-0.314) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.007) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.658 (+/-0.217) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.698 (+/-0.306) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.305) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.662 (+/-0.314) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.585 (+/-0.235) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.007) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.699 (+/-0.308) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.698 (+/-0.306) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.697 (+/-0.303) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.662 (+/-0.314) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.660 (+/-0.315) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.611 (+/-0.238) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.239) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.239) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.007) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.683 (+/-0.296) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.676 (+/-0.244) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.695 (+/-0.301) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.661 (+/-0.312) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.659 (+/-0.312) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.634 (+/-0.324) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.611 (+/-0.240) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.239) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.239) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.007) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.699 (+/-0.307) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.654 (+/-0.242) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.695 (+/-0.302) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.660 (+/-0.313) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.658 (+/-0.310) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.633 (+/-0.326) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.611 (+/-0.240) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.239) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.239) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.007) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.699 (+/-0.307) for {'C': 1.0, 'kernel': 'linear'}
0.696 (+/-0.304) for {'C': 10.0, 'kernel': 'linear'}
0.663 (+/-0.314) for {'C': 100.0, 'kernel': 'linear'}
0.660 (+/-0.316) for {'C': 1000.0, 'kernel': 'linear'}
0.634 (+/-0.321) for {'C': 10000.0, 'kernel': 'linear'}
0.632 (+/-0.323) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6991976873608706
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.652 (+/-0.313) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.642 (+/-0.204) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.622 (+/-0.310) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.642 (+/-0.204) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.622 (+/-0.185) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.598 (+/-0.298) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.642 (+/-0.204) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.618 (+/-0.182) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.625 (+/-0.282) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.636 (+/-0.384) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.642 (+/-0.204) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.618 (+/-0.182) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.597 (+/-0.161) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.605 (+/-0.311) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.638 (+/-0.382) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.642 (+/-0.204) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.618 (+/-0.182) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.597 (+/-0.161) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.587 (+/-0.196) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.608 (+/-0.309) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.638 (+/-0.382) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.747 (+/-0.502) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.642 (+/-0.204) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.622 (+/-0.185) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.597 (+/-0.161) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.583 (+/-0.190) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.580 (+/-0.202) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.610 (+/-0.308) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.638 (+/-0.382) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.697 (+/-0.492) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.651 (+/-0.225) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.604 (+/-0.176) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.600 (+/-0.165) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.587 (+/-0.195) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.589 (+/-0.194) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.581 (+/-0.201) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.610 (+/-0.308) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.638 (+/-0.382) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.831 (+/-0.449) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.535 (+/-0.144) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.607 (+/-0.277) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.591 (+/-0.188) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.588 (+/-0.194) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.584 (+/-0.200) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.583 (+/-0.200) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.610 (+/-0.308) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.638 (+/-0.382) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.793 (+/-0.440) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.551 (+/-0.297) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.609 (+/-0.276) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.588 (+/-0.194) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.587 (+/-0.195) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.584 (+/-0.200) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.583 (+/-0.200) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.610 (+/-0.308) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.638 (+/-0.382) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.798 (+/-0.437) for {'C': 0.1, 'kernel': 'linear'}
0.635 (+/-0.198) for {'C': 1.0, 'kernel': 'linear'}
0.586 (+/-0.152) for {'C': 10.0, 'kernel': 'linear'}
0.587 (+/-0.195) for {'C': 100.0, 'kernel': 'linear'}
0.589 (+/-0.193) for {'C': 1000.0, 'kernel': 'linear'}
0.582 (+/-0.201) for {'C': 10000.0, 'kernel': 'linear'}
0.582 (+/-0.201) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.665 (+/-0.315) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.698 (+/-0.306) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.495 (+/-0.011) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.306) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.304) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.611 (+/-0.239) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.497 (+/-0.008) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.698 (+/-0.306) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.303) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.694 (+/-0.305) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.611 (+/-0.241) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.007) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.698 (+/-0.306) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.303) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.694 (+/-0.305) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.608 (+/-0.243) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.239) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.007) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.698 (+/-0.306) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.303) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.694 (+/-0.305) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.657 (+/-0.312) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.007) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.617 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.698 (+/-0.306) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.697 (+/-0.304) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.694 (+/-0.305) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.658 (+/-0.313) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.609 (+/-0.241) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.007) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.600 (+/-0.245) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.698 (+/-0.307) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.696 (+/-0.300) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.694 (+/-0.304) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.658 (+/-0.313) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.659 (+/-0.313) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.611 (+/-0.238) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.239) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.239) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.007) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.700 (+/-0.309) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.643 (+/-0.209) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.694 (+/-0.302) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.706 (+/-0.339) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.659 (+/-0.312) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.634 (+/-0.324) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.611 (+/-0.240) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.239) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.239) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.007) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.699 (+/-0.307) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.576 (+/-0.232) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.695 (+/-0.301) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.680 (+/-0.365) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.658 (+/-0.311) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.633 (+/-0.326) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.611 (+/-0.240) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.239) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.239) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.007) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.700 (+/-0.309) for {'C': 0.1, 'kernel': 'linear'}
0.698 (+/-0.305) for {'C': 1.0, 'kernel': 'linear'}
0.694 (+/-0.301) for {'C': 10.0, 'kernel': 'linear'}
0.657 (+/-0.313) for {'C': 100.0, 'kernel': 'linear'}
0.659 (+/-0.314) for {'C': 1000.0, 'kernel': 'linear'}
0.633 (+/-0.321) for {'C': 10000.0, 'kernel': 'linear'}
0.632 (+/-0.323) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.07 0.33 0.12 6
1 0.99 0.96 0.98 623
avg / total 0.98 0.95 0.97 629
本轮grid search结果,得到最好的参数选择是: {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.7056069131832797
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.660 (+/-0.219) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.634 (+/-0.193) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.630 (+/-0.189) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.617 (+/-0.185) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.622 (+/-0.185) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.622 (+/-0.184) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.608 (+/-0.161) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.617 (+/-0.181) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.603 (+/-0.196) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.594 (+/-0.191) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.596 (+/-0.193) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.664 (+/-0.291) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.655 (+/-0.286) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.625 (+/-0.184) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.597 (+/-0.161) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.615 (+/-0.167) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.618 (+/-0.185) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.618 (+/-0.184) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.597 (+/-0.191) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.596 (+/-0.191) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.592 (+/-0.192) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.593 (+/-0.195) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.653 (+/-0.286) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.638 (+/-0.287) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.595 (+/-0.160) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.595 (+/-0.154) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.620 (+/-0.184) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.621 (+/-0.190) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.595 (+/-0.189) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.595 (+/-0.192) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.595 (+/-0.194) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.589 (+/-0.194) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.591 (+/-0.195) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.645 (+/-0.286) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.623 (+/-0.289) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.595 (+/-0.160) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.597 (+/-0.155) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.621 (+/-0.187) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.596 (+/-0.192) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.596 (+/-0.191) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.595 (+/-0.192) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.592 (+/-0.194) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.587 (+/-0.195) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.590 (+/-0.194) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.621 (+/-0.289) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.621 (+/-0.290) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.591 (+/-0.154) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.600 (+/-0.158) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.591 (+/-0.177) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.594 (+/-0.192) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.591 (+/-0.187) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.592 (+/-0.193) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.590 (+/-0.195) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.588 (+/-0.195) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.589 (+/-0.194) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.620 (+/-0.290) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.622 (+/-0.289) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.601 (+/-0.164) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.585 (+/-0.184) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.598 (+/-0.194) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.594 (+/-0.191) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.591 (+/-0.193) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.591 (+/-0.194) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.589 (+/-0.195) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.588 (+/-0.194) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.588 (+/-0.194) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.618 (+/-0.290) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.622 (+/-0.289) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.618 (+/-0.211) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.588 (+/-0.184) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.593 (+/-0.193) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.594 (+/-0.191) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.587 (+/-0.170) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.592 (+/-0.195) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.588 (+/-0.194) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.588 (+/-0.194) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.587 (+/-0.195) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.616 (+/-0.291) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.596 (+/-0.175) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.616 (+/-0.211) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.587 (+/-0.184) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.589 (+/-0.189) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.593 (+/-0.191) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.583 (+/-0.164) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.590 (+/-0.195) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.589 (+/-0.194) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.588 (+/-0.194) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.587 (+/-0.195) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.618 (+/-0.290) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.595 (+/-0.175) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.628 (+/-0.225) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.584 (+/-0.184) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.590 (+/-0.187) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.593 (+/-0.191) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.582 (+/-0.164) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.589 (+/-0.195) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.589 (+/-0.194) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.588 (+/-0.194) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.586 (+/-0.195) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.618 (+/-0.290) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.598 (+/-0.175) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.620 (+/-0.212) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.585 (+/-0.184) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.590 (+/-0.187) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.593 (+/-0.191) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.580 (+/-0.163) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.588 (+/-0.195) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.589 (+/-0.194) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.588 (+/-0.194) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.586 (+/-0.195) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.623 (+/-0.289) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.598 (+/-0.175) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.623 (+/-0.216) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.586 (+/-0.185) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.590 (+/-0.187) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.593 (+/-0.191) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.580 (+/-0.163) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.588 (+/-0.195) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.589 (+/-0.194) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.588 (+/-0.195) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.587 (+/-0.195) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.690 (+/-0.300) for {'C': 1.0, 'kernel': 'linear'}
0.613 (+/-0.190) for {'C': 10.0, 'kernel': 'linear'}
0.608 (+/-0.197) for {'C': 100.0, 'kernel': 'linear'}
0.594 (+/-0.194) for {'C': 1000.0, 'kernel': 'linear'}
0.582 (+/-0.201) for {'C': 10000.0, 'kernel': 'linear'}
0.582 (+/-0.201) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.698 (+/-0.306) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.698 (+/-0.306) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.697 (+/-0.305) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.696 (+/-0.304) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.697 (+/-0.304) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.697 (+/-0.303) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.696 (+/-0.304) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.680 (+/-0.291) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.663 (+/-0.314) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.662 (+/-0.311) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.662 (+/-0.314) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.698 (+/-0.305) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.698 (+/-0.305) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.697 (+/-0.304) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.695 (+/-0.303) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.697 (+/-0.305) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.696 (+/-0.301) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.680 (+/-0.294) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.662 (+/-0.312) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.662 (+/-0.313) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.661 (+/-0.313) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.660 (+/-0.315) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.697 (+/-0.304) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.697 (+/-0.304) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.695 (+/-0.303) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.695 (+/-0.304) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.696 (+/-0.306) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.696 (+/-0.301) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.663 (+/-0.313) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.662 (+/-0.314) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.661 (+/-0.315) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.659 (+/-0.312) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.660 (+/-0.314) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.697 (+/-0.303) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.695 (+/-0.302) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.695 (+/-0.303) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.695 (+/-0.303) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.696 (+/-0.306) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.662 (+/-0.309) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.662 (+/-0.313) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.661 (+/-0.315) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.660 (+/-0.315) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.659 (+/-0.311) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.659 (+/-0.313) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.695 (+/-0.302) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.695 (+/-0.302) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.695 (+/-0.304) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.696 (+/-0.304) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.662 (+/-0.314) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.662 (+/-0.310) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.661 (+/-0.314) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.660 (+/-0.314) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.659 (+/-0.315) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.659 (+/-0.312) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.659 (+/-0.312) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.695 (+/-0.301) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.695 (+/-0.301) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.695 (+/-0.305) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.661 (+/-0.310) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.662 (+/-0.314) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.661 (+/-0.312) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.660 (+/-0.313) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.660 (+/-0.314) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.659 (+/-0.314) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.659 (+/-0.312) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.659 (+/-0.312) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.695 (+/-0.301) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.695 (+/-0.301) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.696 (+/-0.307) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.662 (+/-0.312) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.662 (+/-0.313) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.661 (+/-0.313) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.685 (+/-0.369) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.660 (+/-0.314) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.658 (+/-0.313) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.659 (+/-0.312) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.658 (+/-0.310) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.694 (+/-0.301) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.695 (+/-0.301) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.696 (+/-0.307) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.661 (+/-0.311) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.661 (+/-0.313) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.660 (+/-0.313) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.685 (+/-0.368) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.659 (+/-0.313) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.659 (+/-0.313) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.659 (+/-0.312) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.658 (+/-0.310) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.695 (+/-0.301) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.695 (+/-0.300) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.696 (+/-0.307) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.661 (+/-0.311) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.685 (+/-0.367) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.660 (+/-0.313) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.684 (+/-0.368) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.659 (+/-0.312) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.659 (+/-0.313) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.658 (+/-0.313) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.658 (+/-0.310) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.695 (+/-0.301) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.695 (+/-0.301) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.696 (+/-0.307) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.661 (+/-0.311) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.685 (+/-0.367) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.660 (+/-0.313) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.684 (+/-0.367) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.659 (+/-0.312) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.659 (+/-0.314) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.658 (+/-0.313) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.658 (+/-0.310) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.695 (+/-0.302) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.695 (+/-0.301) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.696 (+/-0.308) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.662 (+/-0.312) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.685 (+/-0.367) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.660 (+/-0.313) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.684 (+/-0.367) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.659 (+/-0.312) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.658 (+/-0.314) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.658 (+/-0.312) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.658 (+/-0.310) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.699 (+/-0.307) for {'C': 1.0, 'kernel': 'linear'}
0.696 (+/-0.304) for {'C': 10.0, 'kernel': 'linear'}
0.663 (+/-0.314) for {'C': 100.0, 'kernel': 'linear'}
0.660 (+/-0.316) for {'C': 1000.0, 'kernel': 'linear'}
0.634 (+/-0.321) for {'C': 10000.0, 'kernel': 'linear'}
0.632 (+/-0.323) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.20 0.17 0.18 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.99 0.99 629
本轮grid search结果,得到最好的参数选择是: {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6985561464259213
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.604 (+/-0.176) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.614 (+/-0.292) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.586 (+/-0.153) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.582 (+/-0.145) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.603 (+/-0.163) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.600 (+/-0.165) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.594 (+/-0.162) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.603 (+/-0.182) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.585 (+/-0.196) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.586 (+/-0.195) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.587 (+/-0.195) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.616 (+/-0.291) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.584 (+/-0.177) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.580 (+/-0.144) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.588 (+/-0.153) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.597 (+/-0.161) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.610 (+/-0.184) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.597 (+/-0.180) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.589 (+/-0.193) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.585 (+/-0.196) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.586 (+/-0.196) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.588 (+/-0.195) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.599 (+/-0.278) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.585 (+/-0.176) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.582 (+/-0.145) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.585 (+/-0.152) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.611 (+/-0.183) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.616 (+/-0.182) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.584 (+/-0.189) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.589 (+/-0.193) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.585 (+/-0.196) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.587 (+/-0.195) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.587 (+/-0.195) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.602 (+/-0.278) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.580 (+/-0.150) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.589 (+/-0.153) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.595 (+/-0.155) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.609 (+/-0.192) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.592 (+/-0.188) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.586 (+/-0.196) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.588 (+/-0.194) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.586 (+/-0.196) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.586 (+/-0.195) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.588 (+/-0.194) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.604 (+/-0.277) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.575 (+/-0.143) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.586 (+/-0.149) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.599 (+/-0.149) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.580 (+/-0.171) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.591 (+/-0.188) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.581 (+/-0.191) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.586 (+/-0.195) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.585 (+/-0.196) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.585 (+/-0.196) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.588 (+/-0.194) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.607 (+/-0.277) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.581 (+/-0.144) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.587 (+/-0.152) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.587 (+/-0.177) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.588 (+/-0.194) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.591 (+/-0.188) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.585 (+/-0.197) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.589 (+/-0.194) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.585 (+/-0.196) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.586 (+/-0.195) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.588 (+/-0.194) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.604 (+/-0.277) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.581 (+/-0.144) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.590 (+/-0.155) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.586 (+/-0.178) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.588 (+/-0.194) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.591 (+/-0.189) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.577 (+/-0.174) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.589 (+/-0.193) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.586 (+/-0.196) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.587 (+/-0.194) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.587 (+/-0.195) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.604 (+/-0.277) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.580 (+/-0.144) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.590 (+/-0.155) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.585 (+/-0.178) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.588 (+/-0.183) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.588 (+/-0.194) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.573 (+/-0.167) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.585 (+/-0.196) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.586 (+/-0.195) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.586 (+/-0.195) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.587 (+/-0.195) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.606 (+/-0.277) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.580 (+/-0.144) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.603 (+/-0.180) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.585 (+/-0.178) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.587 (+/-0.183) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.588 (+/-0.194) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.573 (+/-0.167) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.585 (+/-0.196) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.586 (+/-0.195) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.586 (+/-0.195) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.586 (+/-0.195) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.606 (+/-0.277) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.582 (+/-0.146) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.593 (+/-0.160) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.585 (+/-0.178) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.587 (+/-0.184) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.588 (+/-0.194) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.573 (+/-0.167) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.585 (+/-0.196) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.586 (+/-0.195) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.586 (+/-0.196) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.586 (+/-0.195) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.609 (+/-0.276) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.582 (+/-0.146) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.596 (+/-0.167) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.585 (+/-0.178) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.587 (+/-0.184) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.588 (+/-0.194) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.573 (+/-0.167) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.586 (+/-0.196) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.586 (+/-0.195) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.586 (+/-0.196) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.587 (+/-0.195) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.798 (+/-0.437) for {'C': 0.1, 'kernel': 'linear'}
0.635 (+/-0.198) for {'C': 1.0, 'kernel': 'linear'}
0.586 (+/-0.152) for {'C': 10.0, 'kernel': 'linear'}
0.587 (+/-0.195) for {'C': 100.0, 'kernel': 'linear'}
0.589 (+/-0.193) for {'C': 1000.0, 'kernel': 'linear'}
0.582 (+/-0.201) for {'C': 10000.0, 'kernel': 'linear'}
0.582 (+/-0.201) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.696 (+/-0.300) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.694 (+/-0.298) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.694 (+/-0.299) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.694 (+/-0.300) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.695 (+/-0.304) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.694 (+/-0.304) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.716 (+/-0.351) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.699 (+/-0.345) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.656 (+/-0.311) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.657 (+/-0.313) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.658 (+/-0.313) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.694 (+/-0.298) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.692 (+/-0.295) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.694 (+/-0.300) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.694 (+/-0.303) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.694 (+/-0.305) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.717 (+/-0.353) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.697 (+/-0.345) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.682 (+/-0.366) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.656 (+/-0.311) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.656 (+/-0.311) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.658 (+/-0.314) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.693 (+/-0.299) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.693 (+/-0.297) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.694 (+/-0.303) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.693 (+/-0.304) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.717 (+/-0.354) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.742 (+/-0.315) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.680 (+/-0.365) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.681 (+/-0.366) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.656 (+/-0.311) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.657 (+/-0.313) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.658 (+/-0.312) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.694 (+/-0.300) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.693 (+/-0.297) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.694 (+/-0.305) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.718 (+/-0.351) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.716 (+/-0.351) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.707 (+/-0.339) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.655 (+/-0.314) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.681 (+/-0.365) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.657 (+/-0.312) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.657 (+/-0.312) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.658 (+/-0.313) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.694 (+/-0.301) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.693 (+/-0.297) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.694 (+/-0.305) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.742 (+/-0.315) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.681 (+/-0.364) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.706 (+/-0.338) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.655 (+/-0.313) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.656 (+/-0.313) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.656 (+/-0.310) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.657 (+/-0.309) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.659 (+/-0.311) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.694 (+/-0.302) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.694 (+/-0.299) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.694 (+/-0.305) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.708 (+/-0.341) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.680 (+/-0.367) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.706 (+/-0.339) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.655 (+/-0.312) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.681 (+/-0.366) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.656 (+/-0.309) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.657 (+/-0.311) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.659 (+/-0.312) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.694 (+/-0.301) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.694 (+/-0.299) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.694 (+/-0.305) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.708 (+/-0.341) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.680 (+/-0.368) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.705 (+/-0.339) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.655 (+/-0.313) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.681 (+/-0.366) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.657 (+/-0.310) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.658 (+/-0.311) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.658 (+/-0.310) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.694 (+/-0.301) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.693 (+/-0.299) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.694 (+/-0.305) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.708 (+/-0.340) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.705 (+/-0.341) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.680 (+/-0.365) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.655 (+/-0.313) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.656 (+/-0.311) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.657 (+/-0.310) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.658 (+/-0.311) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.658 (+/-0.311) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.694 (+/-0.302) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.693 (+/-0.299) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.694 (+/-0.306) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.708 (+/-0.340) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.705 (+/-0.340) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.680 (+/-0.365) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.655 (+/-0.313) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.656 (+/-0.311) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.657 (+/-0.311) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.657 (+/-0.311) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.658 (+/-0.310) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.694 (+/-0.302) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.694 (+/-0.299) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.694 (+/-0.306) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.708 (+/-0.340) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.705 (+/-0.341) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.680 (+/-0.365) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.655 (+/-0.314) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.656 (+/-0.311) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.657 (+/-0.311) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.657 (+/-0.311) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.658 (+/-0.310) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.695 (+/-0.301) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.694 (+/-0.299) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.694 (+/-0.306) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.708 (+/-0.340) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.705 (+/-0.341) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.680 (+/-0.365) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.655 (+/-0.314) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.656 (+/-0.312) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.657 (+/-0.311) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.657 (+/-0.310) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.658 (+/-0.311) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.700 (+/-0.309) for {'C': 0.1, 'kernel': 'linear'}
0.698 (+/-0.305) for {'C': 1.0, 'kernel': 'linear'}
0.694 (+/-0.301) for {'C': 10.0, 'kernel': 'linear'}
0.657 (+/-0.313) for {'C': 100.0, 'kernel': 'linear'}
0.659 (+/-0.314) for {'C': 1000.0, 'kernel': 'linear'}
0.633 (+/-0.321) for {'C': 10000.0, 'kernel': 'linear'}
0.632 (+/-0.323) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.07 0.17 0.10 6
1 0.99 0.98 0.98 623
avg / total 0.98 0.97 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.7421448594278177
循环迭代之前,delta is: [3.69042656e+06 6.01892829e-06]
执行tunemodel()函数前,使用的grid search parameter是:
[{'C': array([3981071.70553497, 4365158.32240166, 4786300.92322638,
5248074.60249772, 5754399.37337156, 6309573.44480193,
6918309.70918936, 7585775.75029184, 8317637.7110267 ,
9120108.39355909, 9999999.99999999]), 'kernel': ['rbf'], 'gamma': array([2.51188643e-06, 2.75422870e-06, 3.01995172e-06, 3.31131121e-06,
3.63078055e-06, 3.98107171e-06, 4.36515832e-06, 4.78630092e-06,
5.24807460e-06, 5.75439937e-06, 6.30957344e-06])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.595 (+/-0.160) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.651 (+/-0.296) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.594 (+/-0.155) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.636 (+/-0.286) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.602 (+/-0.165) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.597 (+/-0.155) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.600 (+/-0.176) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.648 (+/-0.286) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.597 (+/-0.192) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.626 (+/-0.188) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.621 (+/-0.187) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.597 (+/-0.161) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.651 (+/-0.296) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.596 (+/-0.155) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.632 (+/-0.286) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.602 (+/-0.165) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.596 (+/-0.155) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.602 (+/-0.177) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.656 (+/-0.292) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.597 (+/-0.192) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.626 (+/-0.188) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.604 (+/-0.161) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.591 (+/-0.154) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.649 (+/-0.296) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.594 (+/-0.155) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.633 (+/-0.287) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.598 (+/-0.160) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.597 (+/-0.158) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.607 (+/-0.180) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.645 (+/-0.287) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.597 (+/-0.192) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.626 (+/-0.188) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.604 (+/-0.161) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.595 (+/-0.160) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.649 (+/-0.296) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.592 (+/-0.155) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.635 (+/-0.287) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.609 (+/-0.181) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.596 (+/-0.159) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.610 (+/-0.180) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.645 (+/-0.287) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.597 (+/-0.192) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.627 (+/-0.197) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.603 (+/-0.162) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.595 (+/-0.160) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.652 (+/-0.295) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.593 (+/-0.155) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.643 (+/-0.285) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.609 (+/-0.180) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.600 (+/-0.158) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.607 (+/-0.180) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.639 (+/-0.288) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.596 (+/-0.192) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.622 (+/-0.186) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.603 (+/-0.162) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.591 (+/-0.154) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.652 (+/-0.295) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.593 (+/-0.155) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.649 (+/-0.287) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.609 (+/-0.180) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.600 (+/-0.158) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.592 (+/-0.192) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.639 (+/-0.288) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.596 (+/-0.192) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.609 (+/-0.201) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.591 (+/-0.177) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.591 (+/-0.154) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.652 (+/-0.295) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.592 (+/-0.155) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.646 (+/-0.287) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.609 (+/-0.180) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.600 (+/-0.158) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.592 (+/-0.192) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.628 (+/-0.300) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.598 (+/-0.192) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.609 (+/-0.201) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.589 (+/-0.174) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.595 (+/-0.160) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.644 (+/-0.288) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.597 (+/-0.155) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.638 (+/-0.280) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.609 (+/-0.180) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.591 (+/-0.151) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.592 (+/-0.192) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.603 (+/-0.196) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.598 (+/-0.192) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.609 (+/-0.201) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.598 (+/-0.194) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.597 (+/-0.160) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.650 (+/-0.287) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.599 (+/-0.158) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.636 (+/-0.280) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.612 (+/-0.186) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.595 (+/-0.174) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.590 (+/-0.191) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.612 (+/-0.225) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.597 (+/-0.192) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.609 (+/-0.201) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.598 (+/-0.194) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.600 (+/-0.164) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.650 (+/-0.287) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.599 (+/-0.158) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.638 (+/-0.280) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.612 (+/-0.186) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.595 (+/-0.174) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.590 (+/-0.191) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.606 (+/-0.196) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.594 (+/-0.191) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.609 (+/-0.201) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.598 (+/-0.194) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.601 (+/-0.164) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.650 (+/-0.287) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.600 (+/-0.159) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.636 (+/-0.280) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.612 (+/-0.186) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.585 (+/-0.184) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.586 (+/-0.186) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.610 (+/-0.199) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.594 (+/-0.191) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.609 (+/-0.201) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.598 (+/-0.194) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.690 (+/-0.300) for {'C': 1.0, 'kernel': 'linear'}
0.613 (+/-0.190) for {'C': 10.0, 'kernel': 'linear'}
0.608 (+/-0.197) for {'C': 100.0, 'kernel': 'linear'}
0.594 (+/-0.194) for {'C': 1000.0, 'kernel': 'linear'}
0.582 (+/-0.201) for {'C': 10000.0, 'kernel': 'linear'}
0.582 (+/-0.201) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.695 (+/-0.303) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.697 (+/-0.308) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.695 (+/-0.300) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.696 (+/-0.304) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.695 (+/-0.300) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.695 (+/-0.303) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.678 (+/-0.288) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.697 (+/-0.308) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.662 (+/-0.309) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.680 (+/-0.294) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.696 (+/-0.306) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.695 (+/-0.303) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.697 (+/-0.308) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.695 (+/-0.302) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.696 (+/-0.304) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.695 (+/-0.300) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.695 (+/-0.303) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.679 (+/-0.288) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.697 (+/-0.308) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.662 (+/-0.309) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.680 (+/-0.295) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.679 (+/-0.293) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.695 (+/-0.303) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.697 (+/-0.307) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.695 (+/-0.300) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.696 (+/-0.304) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.695 (+/-0.301) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.695 (+/-0.302) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.679 (+/-0.288) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.697 (+/-0.308) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.662 (+/-0.309) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.680 (+/-0.295) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.679 (+/-0.293) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.695 (+/-0.304) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.697 (+/-0.307) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.695 (+/-0.300) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.696 (+/-0.304) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.695 (+/-0.300) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.695 (+/-0.301) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.679 (+/-0.289) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.697 (+/-0.308) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.662 (+/-0.309) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.680 (+/-0.295) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.679 (+/-0.293) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.695 (+/-0.304) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.697 (+/-0.308) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.695 (+/-0.301) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.697 (+/-0.305) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.695 (+/-0.301) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.696 (+/-0.304) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.679 (+/-0.288) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.680 (+/-0.296) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.662 (+/-0.309) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.680 (+/-0.295) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.679 (+/-0.293) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.695 (+/-0.304) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.697 (+/-0.308) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.695 (+/-0.301) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.697 (+/-0.306) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.695 (+/-0.301) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.696 (+/-0.304) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.662 (+/-0.308) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.680 (+/-0.297) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.662 (+/-0.309) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.663 (+/-0.315) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.662 (+/-0.314) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.695 (+/-0.304) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.697 (+/-0.308) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.695 (+/-0.300) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.697 (+/-0.305) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.695 (+/-0.301) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.696 (+/-0.304) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.662 (+/-0.308) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.663 (+/-0.317) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.663 (+/-0.310) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.663 (+/-0.315) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.662 (+/-0.313) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.695 (+/-0.304) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.697 (+/-0.308) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.696 (+/-0.303) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.696 (+/-0.305) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.695 (+/-0.301) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.679 (+/-0.291) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.662 (+/-0.308) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.663 (+/-0.316) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.663 (+/-0.310) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.663 (+/-0.315) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.662 (+/-0.314) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.695 (+/-0.304) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.697 (+/-0.308) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.696 (+/-0.303) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.696 (+/-0.305) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.695 (+/-0.301) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.678 (+/-0.289) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.662 (+/-0.308) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.663 (+/-0.317) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.662 (+/-0.309) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.663 (+/-0.315) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.662 (+/-0.314) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.695 (+/-0.304) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.697 (+/-0.308) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.696 (+/-0.303) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.696 (+/-0.306) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.695 (+/-0.301) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.678 (+/-0.289) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.662 (+/-0.308) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.663 (+/-0.316) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.662 (+/-0.309) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.663 (+/-0.316) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.662 (+/-0.314) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.695 (+/-0.305) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.697 (+/-0.308) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.696 (+/-0.302) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.680 (+/-0.294) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.695 (+/-0.301) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.661 (+/-0.310) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.662 (+/-0.308) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.663 (+/-0.316) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.662 (+/-0.309) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.663 (+/-0.316) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.662 (+/-0.314) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.699 (+/-0.307) for {'C': 1.0, 'kernel': 'linear'}
0.696 (+/-0.304) for {'C': 10.0, 'kernel': 'linear'}
0.663 (+/-0.314) for {'C': 100.0, 'kernel': 'linear'}
0.660 (+/-0.316) for {'C': 1000.0, 'kernel': 'linear'}
0.634 (+/-0.321) for {'C': 10000.0, 'kernel': 'linear'}
0.632 (+/-0.323) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.20 0.17 0.18 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.99 0.99 629
本轮grid search结果,得到最好的参数选择是: {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6985561464259213
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.589 (+/-0.153) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.608 (+/-0.171) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.588 (+/-0.155) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.630 (+/-0.280) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.592 (+/-0.161) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.595 (+/-0.155) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.593 (+/-0.177) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.600 (+/-0.166) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.594 (+/-0.191) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.605 (+/-0.181) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.609 (+/-0.192) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.590 (+/-0.155) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.608 (+/-0.171) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.588 (+/-0.155) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.625 (+/-0.279) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.590 (+/-0.162) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.595 (+/-0.155) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.595 (+/-0.178) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.608 (+/-0.186) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.592 (+/-0.192) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.604 (+/-0.182) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.593 (+/-0.162) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.590 (+/-0.155) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.606 (+/-0.169) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.588 (+/-0.155) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.621 (+/-0.280) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.589 (+/-0.163) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.596 (+/-0.159) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.599 (+/-0.183) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.597 (+/-0.166) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.592 (+/-0.192) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.604 (+/-0.182) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.593 (+/-0.162) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.590 (+/-0.155) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.603 (+/-0.162) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.586 (+/-0.155) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.626 (+/-0.282) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.599 (+/-0.186) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.594 (+/-0.159) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.606 (+/-0.181) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.597 (+/-0.166) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.591 (+/-0.192) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.604 (+/-0.182) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.593 (+/-0.163) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.583 (+/-0.145) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.606 (+/-0.164) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.586 (+/-0.155) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.632 (+/-0.282) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.599 (+/-0.186) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.599 (+/-0.149) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.606 (+/-0.181) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.590 (+/-0.160) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.591 (+/-0.192) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.604 (+/-0.182) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.593 (+/-0.163) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.586 (+/-0.149) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.606 (+/-0.164) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.587 (+/-0.155) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.635 (+/-0.284) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.599 (+/-0.186) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.599 (+/-0.149) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.593 (+/-0.192) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.589 (+/-0.161) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.591 (+/-0.192) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.590 (+/-0.193) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.580 (+/-0.171) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.583 (+/-0.145) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.606 (+/-0.164) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.586 (+/-0.155) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.634 (+/-0.285) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.599 (+/-0.186) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.599 (+/-0.149) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.593 (+/-0.192) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.578 (+/-0.170) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.591 (+/-0.192) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.589 (+/-0.193) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.580 (+/-0.171) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.587 (+/-0.152) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.606 (+/-0.164) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.587 (+/-0.155) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.634 (+/-0.285) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.599 (+/-0.186) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.590 (+/-0.141) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.592 (+/-0.193) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.577 (+/-0.170) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.590 (+/-0.193) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.589 (+/-0.193) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.588 (+/-0.194) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.589 (+/-0.152) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.612 (+/-0.167) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.590 (+/-0.159) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.634 (+/-0.285) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.602 (+/-0.192) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.598 (+/-0.166) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.589 (+/-0.192) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.577 (+/-0.170) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.590 (+/-0.193) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.589 (+/-0.193) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.588 (+/-0.194) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.588 (+/-0.151) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.612 (+/-0.167) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.590 (+/-0.159) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.634 (+/-0.285) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.602 (+/-0.192) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.597 (+/-0.166) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.588 (+/-0.192) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.579 (+/-0.172) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.587 (+/-0.192) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.589 (+/-0.193) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.588 (+/-0.194) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.587 (+/-0.152) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.612 (+/-0.167) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.593 (+/-0.160) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.627 (+/-0.282) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.602 (+/-0.192) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.587 (+/-0.177) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.584 (+/-0.187) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.579 (+/-0.172) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.588 (+/-0.191) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.592 (+/-0.188) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.588 (+/-0.194) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.798 (+/-0.437) for {'C': 0.1, 'kernel': 'linear'}
0.635 (+/-0.198) for {'C': 1.0, 'kernel': 'linear'}
0.586 (+/-0.152) for {'C': 10.0, 'kernel': 'linear'}
0.587 (+/-0.195) for {'C': 100.0, 'kernel': 'linear'}
0.589 (+/-0.193) for {'C': 1000.0, 'kernel': 'linear'}
0.582 (+/-0.201) for {'C': 10000.0, 'kernel': 'linear'}
0.582 (+/-0.201) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.694 (+/-0.305) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.696 (+/-0.307) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.694 (+/-0.300) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.695 (+/-0.308) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.692 (+/-0.299) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.718 (+/-0.351) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.676 (+/-0.292) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.693 (+/-0.306) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.684 (+/-0.368) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.700 (+/-0.347) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.716 (+/-0.351) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.694 (+/-0.305) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.696 (+/-0.307) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.693 (+/-0.300) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.695 (+/-0.308) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.692 (+/-0.299) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.718 (+/-0.352) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.676 (+/-0.293) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.693 (+/-0.306) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.684 (+/-0.367) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.700 (+/-0.346) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.699 (+/-0.343) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.694 (+/-0.305) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.695 (+/-0.307) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.693 (+/-0.300) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.694 (+/-0.308) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.692 (+/-0.298) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.718 (+/-0.351) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.675 (+/-0.291) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.693 (+/-0.306) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.684 (+/-0.366) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.700 (+/-0.346) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.699 (+/-0.343) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.694 (+/-0.305) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.695 (+/-0.306) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.693 (+/-0.299) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.695 (+/-0.308) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.692 (+/-0.298) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.717 (+/-0.350) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.700 (+/-0.347) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.693 (+/-0.306) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.684 (+/-0.366) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.699 (+/-0.347) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.698 (+/-0.343) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.694 (+/-0.304) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.696 (+/-0.307) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.693 (+/-0.299) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.695 (+/-0.309) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.692 (+/-0.298) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.742 (+/-0.314) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.700 (+/-0.347) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.676 (+/-0.293) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.684 (+/-0.366) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.699 (+/-0.347) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.698 (+/-0.343) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.694 (+/-0.305) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.696 (+/-0.307) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.693 (+/-0.300) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.695 (+/-0.310) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.692 (+/-0.298) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.742 (+/-0.315) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.683 (+/-0.368) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.676 (+/-0.293) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.684 (+/-0.365) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.682 (+/-0.367) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.681 (+/-0.364) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.694 (+/-0.304) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.696 (+/-0.307) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.693 (+/-0.300) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.695 (+/-0.310) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.692 (+/-0.298) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.742 (+/-0.315) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.683 (+/-0.368) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.659 (+/-0.312) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.684 (+/-0.365) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.682 (+/-0.367) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.681 (+/-0.365) for {'C': 6918309.709189363, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.694 (+/-0.304) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.696 (+/-0.307) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.693 (+/-0.301) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.695 (+/-0.310) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.691 (+/-0.299) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.725 (+/-0.313) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.683 (+/-0.367) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.658 (+/-0.312) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.683 (+/-0.364) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.682 (+/-0.367) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.681 (+/-0.365) for {'C': 7585775.750291839, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.694 (+/-0.304) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.696 (+/-0.307) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.693 (+/-0.302) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.694 (+/-0.311) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.692 (+/-0.299) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.725 (+/-0.313) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.682 (+/-0.367) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.658 (+/-0.311) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.683 (+/-0.364) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.682 (+/-0.366) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.681 (+/-0.365) for {'C': 8317637.7110266965, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.694 (+/-0.304) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.696 (+/-0.307) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.693 (+/-0.302) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.694 (+/-0.311) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.691 (+/-0.299) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.725 (+/-0.313) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.682 (+/-0.367) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.658 (+/-0.311) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.683 (+/-0.365) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.681 (+/-0.367) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.680 (+/-0.367) for {'C': 9120108.393559087, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.694 (+/-0.305) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.696 (+/-0.307) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.7542287033381706e-06}
0.693 (+/-0.302) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.019951720402015e-06}
0.678 (+/-0.298) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.3113112148259115e-06}
0.691 (+/-0.299) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.708 (+/-0.341) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.682 (+/-0.367) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.658 (+/-0.312) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.683 (+/-0.366) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.706 (+/-0.340) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 5.754399373371562e-06}
0.680 (+/-0.367) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.700 (+/-0.309) for {'C': 0.1, 'kernel': 'linear'}
0.698 (+/-0.305) for {'C': 1.0, 'kernel': 'linear'}
0.694 (+/-0.301) for {'C': 10.0, 'kernel': 'linear'}
0.657 (+/-0.313) for {'C': 100.0, 'kernel': 'linear'}
0.659 (+/-0.314) for {'C': 1000.0, 'kernel': 'linear'}
0.633 (+/-0.321) for {'C': 10000.0, 'kernel': 'linear'}
0.632 (+/-0.323) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.07 0.17 0.10 6
1 0.99 0.98 0.98 623
avg / total 0.98 0.97 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.7421448594278177
第0轮loop中,tunemodel函数得到的最优参数是: ([{'C': array([5248074.60249772, 5345643.5939697 , 5445026.5284242 ,
5546257.1295791 , 5649369.74812302, 5754399.37337156,
5861381.64514028, 5970352.86583836, 6081350.01278718,
6194410.75076781, 6309573.44480193]), 'kernel': ['rbf'], 'gamma': array([3.63078055e-06, 3.69828180e-06, 3.76703799e-06, 3.83707245e-06,
3.90840896e-06, 3.98107171e-06, 4.05508535e-06, 4.13047502e-06,
4.20726628e-06, 4.28548520e-06, 4.36515832e-06])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}], {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}, 0.7421448594278177)
这是第0次迭代微调C和gamma。
第0次迭代,得到delta: [555174.07143037 0. ]
执行tunemodel()函数前,使用的grid search parameter是:
[{'C': array([5248074.60249772, 5345643.5939697 , 5445026.5284242 ,
5546257.1295791 , 5649369.74812302, 5754399.37337156,
5861381.64514028, 5970352.86583836, 6081350.01278718,
6194410.75076781, 6309573.44480193]), 'kernel': ['rbf'], 'gamma': array([3.63078055e-06, 3.69828180e-06, 3.76703799e-06, 3.83707245e-06,
3.90840896e-06, 3.98107171e-06, 4.05508535e-06, 4.13047502e-06,
4.20726628e-06, 4.28548520e-06, 4.36515832e-06])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.609 (+/-0.181) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.597 (+/-0.161) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.601 (+/-0.160) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.601 (+/-0.159) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.607 (+/-0.163) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.596 (+/-0.159) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.601 (+/-0.183) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.645 (+/-0.294) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.604 (+/-0.160) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.622 (+/-0.198) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.610 (+/-0.180) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.601 (+/-0.161) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.597 (+/-0.161) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.601 (+/-0.160) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.600 (+/-0.159) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.605 (+/-0.163) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.600 (+/-0.158) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.601 (+/-0.183) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.645 (+/-0.294) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.604 (+/-0.160) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.612 (+/-0.212) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.610 (+/-0.180) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.609 (+/-0.181) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.597 (+/-0.161) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.602 (+/-0.160) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.598 (+/-0.158) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.605 (+/-0.163) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.600 (+/-0.158) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.601 (+/-0.183) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.648 (+/-0.293) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.604 (+/-0.160) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.612 (+/-0.212) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.609 (+/-0.180) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.600 (+/-0.161) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.599 (+/-0.162) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.602 (+/-0.160) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.600 (+/-0.159) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.605 (+/-0.163) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.600 (+/-0.158) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.601 (+/-0.183) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.648 (+/-0.293) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.604 (+/-0.160) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.612 (+/-0.212) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.607 (+/-0.180) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.609 (+/-0.180) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.599 (+/-0.162) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.607 (+/-0.164) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.600 (+/-0.159) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.605 (+/-0.163) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.600 (+/-0.158) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.601 (+/-0.183) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.648 (+/-0.293) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.610 (+/-0.164) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.612 (+/-0.212) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.607 (+/-0.180) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.609 (+/-0.180) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.608 (+/-0.182) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.607 (+/-0.164) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.600 (+/-0.159) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.605 (+/-0.163) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.600 (+/-0.158) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.601 (+/-0.183) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.648 (+/-0.293) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.610 (+/-0.164) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.612 (+/-0.212) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.607 (+/-0.180) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.609 (+/-0.180) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.608 (+/-0.182) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.607 (+/-0.164) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.595 (+/-0.157) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.605 (+/-0.163) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.601 (+/-0.159) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.610 (+/-0.164) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.635 (+/-0.307) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.610 (+/-0.164) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.612 (+/-0.212) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.605 (+/-0.181) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.609 (+/-0.180) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.608 (+/-0.182) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.607 (+/-0.164) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.595 (+/-0.157) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.605 (+/-0.163) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.600 (+/-0.158) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.610 (+/-0.164) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.635 (+/-0.307) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.610 (+/-0.164) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.620 (+/-0.238) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.592 (+/-0.192) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.609 (+/-0.180) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.608 (+/-0.182) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.607 (+/-0.164) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.595 (+/-0.157) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.605 (+/-0.163) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.600 (+/-0.158) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.598 (+/-0.177) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.635 (+/-0.307) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.627 (+/-0.210) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.618 (+/-0.238) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.592 (+/-0.192) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.609 (+/-0.181) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.608 (+/-0.182) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.607 (+/-0.164) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.596 (+/-0.157) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.605 (+/-0.163) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.600 (+/-0.158) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.598 (+/-0.177) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.635 (+/-0.307) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.627 (+/-0.210) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.609 (+/-0.212) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.592 (+/-0.192) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.609 (+/-0.180) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.612 (+/-0.185) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.607 (+/-0.164) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.596 (+/-0.157) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.605 (+/-0.163) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.600 (+/-0.158) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.601 (+/-0.183) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.635 (+/-0.307) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.610 (+/-0.164) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.612 (+/-0.212) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.592 (+/-0.192) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.690 (+/-0.300) for {'C': 1.0, 'kernel': 'linear'}
0.613 (+/-0.190) for {'C': 10.0, 'kernel': 'linear'}
0.608 (+/-0.197) for {'C': 100.0, 'kernel': 'linear'}
0.594 (+/-0.194) for {'C': 1000.0, 'kernel': 'linear'}
0.582 (+/-0.201) for {'C': 10000.0, 'kernel': 'linear'}
0.582 (+/-0.201) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.695 (+/-0.300) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.694 (+/-0.298) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.695 (+/-0.301) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.696 (+/-0.303) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.696 (+/-0.303) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.695 (+/-0.301) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.663 (+/-0.315) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.680 (+/-0.296) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.680 (+/-0.293) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.680 (+/-0.295) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.679 (+/-0.289) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.695 (+/-0.301) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.694 (+/-0.298) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.695 (+/-0.301) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.696 (+/-0.302) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.696 (+/-0.302) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.696 (+/-0.304) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.663 (+/-0.315) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.680 (+/-0.296) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.680 (+/-0.293) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.663 (+/-0.316) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.679 (+/-0.289) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.695 (+/-0.300) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.694 (+/-0.298) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.696 (+/-0.302) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.695 (+/-0.302) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.696 (+/-0.302) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.696 (+/-0.304) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.663 (+/-0.315) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.681 (+/-0.296) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.680 (+/-0.293) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.663 (+/-0.316) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.679 (+/-0.288) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.695 (+/-0.300) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.695 (+/-0.298) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.696 (+/-0.302) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.696 (+/-0.302) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.696 (+/-0.302) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.696 (+/-0.304) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.663 (+/-0.315) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.681 (+/-0.296) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.680 (+/-0.293) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.663 (+/-0.316) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.679 (+/-0.288) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.695 (+/-0.301) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.695 (+/-0.298) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.696 (+/-0.302) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.696 (+/-0.302) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.696 (+/-0.303) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.696 (+/-0.304) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.663 (+/-0.315) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.681 (+/-0.296) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.696 (+/-0.306) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.663 (+/-0.316) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.679 (+/-0.288) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.695 (+/-0.301) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.695 (+/-0.298) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.696 (+/-0.302) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.696 (+/-0.302) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.696 (+/-0.302) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.696 (+/-0.304) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.663 (+/-0.315) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.681 (+/-0.296) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.696 (+/-0.306) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.663 (+/-0.316) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.679 (+/-0.288) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.695 (+/-0.301) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.695 (+/-0.298) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.696 (+/-0.302) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.695 (+/-0.302) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.696 (+/-0.302) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.696 (+/-0.304) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.680 (+/-0.293) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.664 (+/-0.317) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.696 (+/-0.306) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.663 (+/-0.316) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.679 (+/-0.288) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.695 (+/-0.301) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.695 (+/-0.298) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.696 (+/-0.302) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.695 (+/-0.302) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.696 (+/-0.302) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.696 (+/-0.304) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.680 (+/-0.293) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.664 (+/-0.317) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.696 (+/-0.306) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.664 (+/-0.316) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.662 (+/-0.308) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.695 (+/-0.301) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.695 (+/-0.298) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.696 (+/-0.302) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.695 (+/-0.302) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.696 (+/-0.303) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.696 (+/-0.304) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.663 (+/-0.314) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.664 (+/-0.317) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.697 (+/-0.308) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.663 (+/-0.316) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.662 (+/-0.308) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.695 (+/-0.300) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.695 (+/-0.298) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.696 (+/-0.302) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.695 (+/-0.302) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.696 (+/-0.303) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.696 (+/-0.304) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.663 (+/-0.314) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.664 (+/-0.317) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.697 (+/-0.308) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.663 (+/-0.315) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.662 (+/-0.308) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.695 (+/-0.301) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.695 (+/-0.298) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.696 (+/-0.302) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.695 (+/-0.302) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.696 (+/-0.303) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.696 (+/-0.304) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.663 (+/-0.315) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.664 (+/-0.317) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.696 (+/-0.306) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.663 (+/-0.316) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.662 (+/-0.308) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.699 (+/-0.307) for {'C': 1.0, 'kernel': 'linear'}
0.696 (+/-0.304) for {'C': 10.0, 'kernel': 'linear'}
0.663 (+/-0.314) for {'C': 100.0, 'kernel': 'linear'}
0.660 (+/-0.316) for {'C': 1000.0, 'kernel': 'linear'}
0.634 (+/-0.321) for {'C': 10000.0, 'kernel': 'linear'}
0.632 (+/-0.323) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.20 0.17 0.18 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.99 0.99 629
本轮grid search结果,得到最好的参数选择是: {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6985561464259213
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.599 (+/-0.186) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.591 (+/-0.161) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.595 (+/-0.161) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.593 (+/-0.160) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.598 (+/-0.165) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.594 (+/-0.159) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.581 (+/-0.171) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.597 (+/-0.178) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.590 (+/-0.154) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.600 (+/-0.180) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.606 (+/-0.181) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.591 (+/-0.164) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.591 (+/-0.161) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.594 (+/-0.162) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.592 (+/-0.160) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.598 (+/-0.165) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.594 (+/-0.159) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.581 (+/-0.171) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.597 (+/-0.178) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.590 (+/-0.154) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.590 (+/-0.191) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.606 (+/-0.181) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.599 (+/-0.186) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.591 (+/-0.161) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.593 (+/-0.162) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.591 (+/-0.161) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.598 (+/-0.165) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.600 (+/-0.148) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.581 (+/-0.171) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.604 (+/-0.176) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.589 (+/-0.154) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.590 (+/-0.191) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.605 (+/-0.181) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.591 (+/-0.165) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.593 (+/-0.163) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.593 (+/-0.162) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.591 (+/-0.161) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.598 (+/-0.165) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.599 (+/-0.149) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.581 (+/-0.171) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.604 (+/-0.176) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.588 (+/-0.154) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.589 (+/-0.191) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.605 (+/-0.181) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.599 (+/-0.186) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.593 (+/-0.163) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.598 (+/-0.167) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.591 (+/-0.161) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.598 (+/-0.165) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.599 (+/-0.149) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.581 (+/-0.171) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.604 (+/-0.176) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.595 (+/-0.161) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.589 (+/-0.191) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.606 (+/-0.181) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.599 (+/-0.186) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.601 (+/-0.184) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.598 (+/-0.167) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.591 (+/-0.161) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.598 (+/-0.165) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.599 (+/-0.149) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.581 (+/-0.171) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.604 (+/-0.176) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.595 (+/-0.161) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.589 (+/-0.191) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.606 (+/-0.181) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.599 (+/-0.186) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.601 (+/-0.184) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.598 (+/-0.167) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.589 (+/-0.160) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.598 (+/-0.165) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.599 (+/-0.149) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.593 (+/-0.162) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.590 (+/-0.187) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.595 (+/-0.161) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.589 (+/-0.191) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.605 (+/-0.181) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.599 (+/-0.186) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.601 (+/-0.184) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.598 (+/-0.167) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.589 (+/-0.160) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.598 (+/-0.165) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.599 (+/-0.149) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.593 (+/-0.162) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.591 (+/-0.187) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.595 (+/-0.161) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.589 (+/-0.191) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.593 (+/-0.192) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.599 (+/-0.186) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.601 (+/-0.184) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.598 (+/-0.167) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.589 (+/-0.160) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.598 (+/-0.165) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.599 (+/-0.149) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.581 (+/-0.171) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.591 (+/-0.187) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.595 (+/-0.161) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.589 (+/-0.191) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.593 (+/-0.192) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.599 (+/-0.186) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.601 (+/-0.184) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.598 (+/-0.167) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.589 (+/-0.160) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.598 (+/-0.165) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.599 (+/-0.149) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.581 (+/-0.171) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.590 (+/-0.187) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.595 (+/-0.161) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.589 (+/-0.191) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.593 (+/-0.192) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.599 (+/-0.186) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.605 (+/-0.188) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.598 (+/-0.167) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.589 (+/-0.160) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.598 (+/-0.165) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.599 (+/-0.149) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.581 (+/-0.171) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.590 (+/-0.187) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.595 (+/-0.161) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.589 (+/-0.191) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.593 (+/-0.192) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.798 (+/-0.437) for {'C': 0.1, 'kernel': 'linear'}
0.635 (+/-0.198) for {'C': 1.0, 'kernel': 'linear'}
0.586 (+/-0.152) for {'C': 10.0, 'kernel': 'linear'}
0.587 (+/-0.195) for {'C': 100.0, 'kernel': 'linear'}
0.589 (+/-0.193) for {'C': 1000.0, 'kernel': 'linear'}
0.582 (+/-0.201) for {'C': 10000.0, 'kernel': 'linear'}
0.582 (+/-0.201) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.692 (+/-0.298) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.693 (+/-0.302) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.693 (+/-0.303) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.694 (+/-0.303) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.718 (+/-0.349) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.717 (+/-0.350) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.660 (+/-0.310) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.677 (+/-0.292) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.677 (+/-0.292) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.677 (+/-0.292) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.700 (+/-0.347) for {'C': 5248074.6024977155, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.692 (+/-0.298) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.692 (+/-0.301) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.693 (+/-0.302) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.694 (+/-0.302) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.718 (+/-0.349) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.717 (+/-0.350) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.660 (+/-0.310) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.677 (+/-0.292) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.677 (+/-0.292) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.661 (+/-0.312) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.700 (+/-0.347) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.692 (+/-0.298) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.692 (+/-0.301) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.693 (+/-0.302) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.693 (+/-0.302) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.718 (+/-0.349) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.742 (+/-0.315) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.660 (+/-0.310) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.702 (+/-0.346) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.677 (+/-0.292) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.661 (+/-0.312) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.700 (+/-0.346) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.692 (+/-0.298) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.693 (+/-0.301) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.693 (+/-0.302) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.693 (+/-0.302) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.718 (+/-0.349) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.742 (+/-0.315) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.660 (+/-0.310) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.702 (+/-0.346) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.677 (+/-0.291) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.661 (+/-0.312) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.700 (+/-0.346) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.692 (+/-0.298) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.693 (+/-0.301) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.693 (+/-0.302) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.693 (+/-0.302) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.717 (+/-0.349) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.742 (+/-0.314) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.660 (+/-0.310) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.702 (+/-0.346) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.693 (+/-0.304) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.661 (+/-0.312) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.700 (+/-0.347) for {'C': 5649369.748123019, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.692 (+/-0.298) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.692 (+/-0.300) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.693 (+/-0.302) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.693 (+/-0.302) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.717 (+/-0.349) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.742 (+/-0.314) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.660 (+/-0.310) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.702 (+/-0.346) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.693 (+/-0.304) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.660 (+/-0.312) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.700 (+/-0.347) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.692 (+/-0.298) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.692 (+/-0.300) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.693 (+/-0.302) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.693 (+/-0.303) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.717 (+/-0.349) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.742 (+/-0.314) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.676 (+/-0.290) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.685 (+/-0.367) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.693 (+/-0.304) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.660 (+/-0.312) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.700 (+/-0.347) for {'C': 5861381.645140276, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.692 (+/-0.298) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.692 (+/-0.300) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.693 (+/-0.302) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.693 (+/-0.303) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.717 (+/-0.349) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.742 (+/-0.314) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.676 (+/-0.291) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.685 (+/-0.368) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.693 (+/-0.304) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.660 (+/-0.312) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.683 (+/-0.368) for {'C': 5970352.865838355, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.692 (+/-0.298) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.692 (+/-0.300) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.693 (+/-0.302) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.693 (+/-0.303) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.717 (+/-0.349) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.742 (+/-0.315) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.659 (+/-0.310) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.685 (+/-0.368) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.693 (+/-0.304) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.660 (+/-0.312) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.683 (+/-0.368) for {'C': 6081350.012787178, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.692 (+/-0.298) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.692 (+/-0.300) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.693 (+/-0.302) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.693 (+/-0.303) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.717 (+/-0.349) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.742 (+/-0.315) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.659 (+/-0.311) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.685 (+/-0.367) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.693 (+/-0.304) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.660 (+/-0.312) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.683 (+/-0.368) for {'C': 6194410.750767811, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.692 (+/-0.298) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.630780547701016e-06}
0.693 (+/-0.300) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.6982817978026634e-06}
0.693 (+/-0.302) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.7670379898390884e-06}
0.693 (+/-0.303) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.837072454922786e-06}
0.717 (+/-0.349) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.742 (+/-0.315) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.659 (+/-0.311) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.685 (+/-0.367) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 4.1304750199016155e-06}
0.693 (+/-0.304) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 4.207266283844441e-06}
0.660 (+/-0.312) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 4.285485203974394e-06}
0.683 (+/-0.368) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 4.3651583224016575e-06}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.700 (+/-0.309) for {'C': 0.1, 'kernel': 'linear'}
0.698 (+/-0.305) for {'C': 1.0, 'kernel': 'linear'}
0.694 (+/-0.301) for {'C': 10.0, 'kernel': 'linear'}
0.657 (+/-0.313) for {'C': 100.0, 'kernel': 'linear'}
0.659 (+/-0.314) for {'C': 1000.0, 'kernel': 'linear'}
0.633 (+/-0.321) for {'C': 10000.0, 'kernel': 'linear'}
0.632 (+/-0.323) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.07 0.17 0.10 6
1 0.99 0.98 0.98 623
avg / total 0.98 0.97 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.7424653722483305
第1轮loop中,tunemodel函数得到的最优参数是: ([{'C': array([5345643.5939697 , 5365373.99519851, 5385177.21997526,
5405053.53708668, 5425003.21631149, 5445026.5284242 ,
5465123.74519871, 5485295.13941203, 5505540.98484794,
5525861.55630077, 5546257.1295791 ]), 'kernel': ['rbf'], 'gamma': array([3.90840896e-06, 3.92283463e-06, 3.93731354e-06, 3.95184589e-06,
3.96643188e-06, 3.98107171e-06, 3.99576557e-06, 4.01051366e-06,
4.02531619e-06, 4.04017335e-06, 4.05508535e-06])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}], {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}, 0.7424653722483305)
这是第1次迭代微调C和gamma。
第1次迭代,得到delta: [309372.84494736 0. ]
执行tunemodel()函数前,使用的grid search parameter是:
[{'C': array([5345643.5939697 , 5365373.99519851, 5385177.21997526,
5405053.53708668, 5425003.21631149, 5445026.5284242 ,
5465123.74519871, 5485295.13941203, 5505540.98484794,
5525861.55630077, 5546257.1295791 ]), 'kernel': ['rbf'], 'gamma': array([3.90840896e-06, 3.92283463e-06, 3.93731354e-06, 3.95184589e-06,
3.96643188e-06, 3.98107171e-06, 3.99576557e-06, 4.01051366e-06,
4.02531619e-06, 4.04017335e-06, 4.05508535e-06])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.605 (+/-0.163) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.604 (+/-0.163) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.922834625395207e-06}
0.610 (+/-0.163) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.937313537008499e-06}
0.611 (+/-0.179) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.951845889284363e-06}
0.605 (+/-0.179) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.966431879468585e-06}
0.600 (+/-0.158) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.597 (+/-0.160) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.995765566188054e-06}
0.604 (+/-0.159) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 4.01051366086576e-06}
0.615 (+/-0.179) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 4.025316189742124e-06}
0.619 (+/-0.187) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 4.0401733537300044e-06}
0.601 (+/-0.183) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.605 (+/-0.163) for {'C': 5365373.99519851, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.604 (+/-0.163) for {'C': 5365373.99519851, 'kernel': 'rbf', 'gamma': 3.922834625395207e-06}
0.608 (+/-0.161) for {'C': 5365373.99519851, 'kernel': 'rbf', 'gamma': 3.937313537008499e-06}
0.611 (+/-0.179) for {'C': 5365373.99519851, 'kernel': 'rbf', 'gamma': 3.951845889284363e-06}
0.599 (+/-0.175) for {'C': 5365373.99519851, 'kernel': 'rbf', 'gamma': 3.966431879468585e-06}
0.600 (+/-0.158) for {'C': 5365373.99519851, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.597 (+/-0.160) for {'C': 5365373.99519851, 'kernel': 'rbf', 'gamma': 3.995765566188054e-06}
0.597 (+/-0.154) for {'C': 5365373.99519851, 'kernel': 'rbf', 'gamma': 4.01051366086576e-06}
0.615 (+/-0.179) for {'C': 5365373.99519851, 'kernel': 'rbf', 'gamma': 4.025316189742124e-06}
0.614 (+/-0.180) for {'C': 5365373.99519851, 'kernel': 'rbf', 'gamma': 4.0401733537300044e-06}
0.601 (+/-0.183) for {'C': 5365373.99519851, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.605 (+/-0.163) for {'C': 5385177.219975264, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.604 (+/-0.163) for {'C': 5385177.219975264, 'kernel': 'rbf', 'gamma': 3.922834625395207e-06}
0.608 (+/-0.161) for {'C': 5385177.219975264, 'kernel': 'rbf', 'gamma': 3.937313537008499e-06}
0.611 (+/-0.179) for {'C': 5385177.219975264, 'kernel': 'rbf', 'gamma': 3.951845889284363e-06}
0.599 (+/-0.175) for {'C': 5385177.219975264, 'kernel': 'rbf', 'gamma': 3.966431879468585e-06}
0.600 (+/-0.158) for {'C': 5385177.219975264, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.597 (+/-0.160) for {'C': 5385177.219975264, 'kernel': 'rbf', 'gamma': 3.995765566188054e-06}
0.597 (+/-0.154) for {'C': 5385177.219975264, 'kernel': 'rbf', 'gamma': 4.01051366086576e-06}
0.615 (+/-0.179) for {'C': 5385177.219975264, 'kernel': 'rbf', 'gamma': 4.025316189742124e-06}
0.614 (+/-0.180) for {'C': 5385177.219975264, 'kernel': 'rbf', 'gamma': 4.0401733537300044e-06}
0.601 (+/-0.183) for {'C': 5385177.219975264, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.605 (+/-0.163) for {'C': 5405053.537086678, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.604 (+/-0.163) for {'C': 5405053.537086678, 'kernel': 'rbf', 'gamma': 3.922834625395207e-06}
0.608 (+/-0.161) for {'C': 5405053.537086678, 'kernel': 'rbf', 'gamma': 3.937313537008499e-06}
0.611 (+/-0.179) for {'C': 5405053.537086678, 'kernel': 'rbf', 'gamma': 3.951845889284363e-06}
0.599 (+/-0.175) for {'C': 5405053.537086678, 'kernel': 'rbf', 'gamma': 3.966431879468585e-06}
0.600 (+/-0.158) for {'C': 5405053.537086678, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.597 (+/-0.160) for {'C': 5405053.537086678, 'kernel': 'rbf', 'gamma': 3.995765566188054e-06}
0.597 (+/-0.154) for {'C': 5405053.537086678, 'kernel': 'rbf', 'gamma': 4.01051366086576e-06}
0.615 (+/-0.179) for {'C': 5405053.537086678, 'kernel': 'rbf', 'gamma': 4.025316189742124e-06}
0.614 (+/-0.180) for {'C': 5405053.537086678, 'kernel': 'rbf', 'gamma': 4.0401733537300044e-06}
0.601 (+/-0.183) for {'C': 5405053.537086678, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.605 (+/-0.163) for {'C': 5425003.216311495, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.604 (+/-0.163) for {'C': 5425003.216311495, 'kernel': 'rbf', 'gamma': 3.922834625395207e-06}
0.608 (+/-0.161) for {'C': 5425003.216311495, 'kernel': 'rbf', 'gamma': 3.937313537008499e-06}
0.611 (+/-0.179) for {'C': 5425003.216311495, 'kernel': 'rbf', 'gamma': 3.951845889284363e-06}
0.599 (+/-0.175) for {'C': 5425003.216311495, 'kernel': 'rbf', 'gamma': 3.966431879468585e-06}
0.600 (+/-0.158) for {'C': 5425003.216311495, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.597 (+/-0.160) for {'C': 5425003.216311495, 'kernel': 'rbf', 'gamma': 3.995765566188054e-06}
0.597 (+/-0.154) for {'C': 5425003.216311495, 'kernel': 'rbf', 'gamma': 4.01051366086576e-06}
0.615 (+/-0.179) for {'C': 5425003.216311495, 'kernel': 'rbf', 'gamma': 4.025316189742124e-06}
0.614 (+/-0.180) for {'C': 5425003.216311495, 'kernel': 'rbf', 'gamma': 4.0401733537300044e-06}
0.601 (+/-0.183) for {'C': 5425003.216311495, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.605 (+/-0.163) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.604 (+/-0.163) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.922834625395207e-06}
0.610 (+/-0.163) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.937313537008499e-06}
0.611 (+/-0.179) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.951845889284363e-06}
0.599 (+/-0.175) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.966431879468585e-06}
0.600 (+/-0.158) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.597 (+/-0.160) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.995765566188054e-06}
0.597 (+/-0.154) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 4.01051366086576e-06}
0.615 (+/-0.179) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 4.025316189742124e-06}
0.619 (+/-0.187) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 4.0401733537300044e-06}
0.601 (+/-0.183) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.605 (+/-0.163) for {'C': 5465123.745198711, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.604 (+/-0.163) for {'C': 5465123.745198711, 'kernel': 'rbf', 'gamma': 3.922834625395207e-06}
0.610 (+/-0.163) for {'C': 5465123.745198711, 'kernel': 'rbf', 'gamma': 3.937313537008499e-06}
0.611 (+/-0.179) for {'C': 5465123.745198711, 'kernel': 'rbf', 'gamma': 3.951845889284363e-06}
0.605 (+/-0.179) for {'C': 5465123.745198711, 'kernel': 'rbf', 'gamma': 3.966431879468585e-06}
0.600 (+/-0.158) for {'C': 5465123.745198711, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.597 (+/-0.160) for {'C': 5465123.745198711, 'kernel': 'rbf', 'gamma': 3.995765566188054e-06}
0.597 (+/-0.154) for {'C': 5465123.745198711, 'kernel': 'rbf', 'gamma': 4.01051366086576e-06}
0.615 (+/-0.179) for {'C': 5465123.745198711, 'kernel': 'rbf', 'gamma': 4.025316189742124e-06}
0.619 (+/-0.187) for {'C': 5465123.745198711, 'kernel': 'rbf', 'gamma': 4.0401733537300044e-06}
0.601 (+/-0.183) for {'C': 5465123.745198711, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.605 (+/-0.163) for {'C': 5485295.139412026, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.604 (+/-0.163) for {'C': 5485295.139412026, 'kernel': 'rbf', 'gamma': 3.922834625395207e-06}
0.608 (+/-0.161) for {'C': 5485295.139412026, 'kernel': 'rbf', 'gamma': 3.937313537008499e-06}
0.611 (+/-0.179) for {'C': 5485295.139412026, 'kernel': 'rbf', 'gamma': 3.951845889284363e-06}
0.599 (+/-0.175) for {'C': 5485295.139412026, 'kernel': 'rbf', 'gamma': 3.966431879468585e-06}
0.600 (+/-0.158) for {'C': 5485295.139412026, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.598 (+/-0.160) for {'C': 5485295.139412026, 'kernel': 'rbf', 'gamma': 3.995765566188054e-06}
0.597 (+/-0.154) for {'C': 5485295.139412026, 'kernel': 'rbf', 'gamma': 4.01051366086576e-06}
0.615 (+/-0.179) for {'C': 5485295.139412026, 'kernel': 'rbf', 'gamma': 4.025316189742124e-06}
0.614 (+/-0.180) for {'C': 5485295.139412026, 'kernel': 'rbf', 'gamma': 4.0401733537300044e-06}
0.601 (+/-0.183) for {'C': 5485295.139412026, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.605 (+/-0.163) for {'C': 5505540.984847944, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.604 (+/-0.163) for {'C': 5505540.984847944, 'kernel': 'rbf', 'gamma': 3.922834625395207e-06}
0.607 (+/-0.161) for {'C': 5505540.984847944, 'kernel': 'rbf', 'gamma': 3.937313537008499e-06}
0.611 (+/-0.179) for {'C': 5505540.984847944, 'kernel': 'rbf', 'gamma': 3.951845889284363e-06}
0.599 (+/-0.175) for {'C': 5505540.984847944, 'kernel': 'rbf', 'gamma': 3.966431879468585e-06}
0.600 (+/-0.158) for {'C': 5505540.984847944, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.598 (+/-0.160) for {'C': 5505540.984847944, 'kernel': 'rbf', 'gamma': 3.995765566188054e-06}
0.597 (+/-0.154) for {'C': 5505540.984847944, 'kernel': 'rbf', 'gamma': 4.01051366086576e-06}
0.615 (+/-0.179) for {'C': 5505540.984847944, 'kernel': 'rbf', 'gamma': 4.025316189742124e-06}
0.619 (+/-0.187) for {'C': 5505540.984847944, 'kernel': 'rbf', 'gamma': 4.0401733537300044e-06}
0.601 (+/-0.183) for {'C': 5505540.984847944, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.605 (+/-0.163) for {'C': 5525861.556300772, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.604 (+/-0.163) for {'C': 5525861.556300772, 'kernel': 'rbf', 'gamma': 3.922834625395207e-06}
0.607 (+/-0.161) for {'C': 5525861.556300772, 'kernel': 'rbf', 'gamma': 3.937313537008499e-06}
0.611 (+/-0.179) for {'C': 5525861.556300772, 'kernel': 'rbf', 'gamma': 3.951845889284363e-06}
0.599 (+/-0.175) for {'C': 5525861.556300772, 'kernel': 'rbf', 'gamma': 3.966431879468585e-06}
0.600 (+/-0.158) for {'C': 5525861.556300772, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.598 (+/-0.160) for {'C': 5525861.556300772, 'kernel': 'rbf', 'gamma': 3.995765566188054e-06}
0.597 (+/-0.154) for {'C': 5525861.556300772, 'kernel': 'rbf', 'gamma': 4.01051366086576e-06}
0.619 (+/-0.181) for {'C': 5525861.556300772, 'kernel': 'rbf', 'gamma': 4.025316189742124e-06}
0.614 (+/-0.180) for {'C': 5525861.556300772, 'kernel': 'rbf', 'gamma': 4.0401733537300044e-06}
0.601 (+/-0.183) for {'C': 5525861.556300772, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.605 (+/-0.163) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.604 (+/-0.163) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.922834625395207e-06}
0.607 (+/-0.161) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.937313537008499e-06}
0.611 (+/-0.179) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.951845889284363e-06}
0.599 (+/-0.175) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.966431879468585e-06}
0.600 (+/-0.158) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.600 (+/-0.159) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.995765566188054e-06}
0.597 (+/-0.154) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 4.01051366086576e-06}
0.615 (+/-0.179) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 4.025316189742124e-06}
0.614 (+/-0.180) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 4.0401733537300044e-06}
0.601 (+/-0.183) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.690 (+/-0.300) for {'C': 1.0, 'kernel': 'linear'}
0.613 (+/-0.190) for {'C': 10.0, 'kernel': 'linear'}
0.608 (+/-0.197) for {'C': 100.0, 'kernel': 'linear'}
0.594 (+/-0.194) for {'C': 1000.0, 'kernel': 'linear'}
0.582 (+/-0.201) for {'C': 10000.0, 'kernel': 'linear'}
0.582 (+/-0.201) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.696 (+/-0.302) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.696 (+/-0.303) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.922834625395207e-06}
0.696 (+/-0.304) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.937313537008499e-06}
0.696 (+/-0.303) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.951845889284363e-06}
0.695 (+/-0.303) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.966431879468585e-06}
0.696 (+/-0.304) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.695 (+/-0.301) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.995765566188054e-06}
0.696 (+/-0.304) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 4.01051366086576e-06}
0.696 (+/-0.305) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 4.025316189742124e-06}
0.680 (+/-0.294) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 4.0401733537300044e-06}
0.663 (+/-0.315) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.696 (+/-0.302) for {'C': 5365373.99519851, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.696 (+/-0.303) for {'C': 5365373.99519851, 'kernel': 'rbf', 'gamma': 3.922834625395207e-06}
0.696 (+/-0.304) for {'C': 5365373.99519851, 'kernel': 'rbf', 'gamma': 3.937313537008499e-06}
0.696 (+/-0.303) for {'C': 5365373.99519851, 'kernel': 'rbf', 'gamma': 3.951845889284363e-06}
0.679 (+/-0.290) for {'C': 5365373.99519851, 'kernel': 'rbf', 'gamma': 3.966431879468585e-06}
0.696 (+/-0.304) for {'C': 5365373.99519851, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.695 (+/-0.301) for {'C': 5365373.99519851, 'kernel': 'rbf', 'gamma': 3.995765566188054e-06}
0.679 (+/-0.292) for {'C': 5365373.99519851, 'kernel': 'rbf', 'gamma': 4.01051366086576e-06}
0.696 (+/-0.305) for {'C': 5365373.99519851, 'kernel': 'rbf', 'gamma': 4.025316189742124e-06}
0.680 (+/-0.293) for {'C': 5365373.99519851, 'kernel': 'rbf', 'gamma': 4.0401733537300044e-06}
0.663 (+/-0.315) for {'C': 5365373.99519851, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.696 (+/-0.302) for {'C': 5385177.219975264, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.696 (+/-0.303) for {'C': 5385177.219975264, 'kernel': 'rbf', 'gamma': 3.922834625395207e-06}
0.696 (+/-0.304) for {'C': 5385177.219975264, 'kernel': 'rbf', 'gamma': 3.937313537008499e-06}
0.696 (+/-0.303) for {'C': 5385177.219975264, 'kernel': 'rbf', 'gamma': 3.951845889284363e-06}
0.679 (+/-0.290) for {'C': 5385177.219975264, 'kernel': 'rbf', 'gamma': 3.966431879468585e-06}
0.696 (+/-0.304) for {'C': 5385177.219975264, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.695 (+/-0.301) for {'C': 5385177.219975264, 'kernel': 'rbf', 'gamma': 3.995765566188054e-06}
0.679 (+/-0.292) for {'C': 5385177.219975264, 'kernel': 'rbf', 'gamma': 4.01051366086576e-06}
0.696 (+/-0.305) for {'C': 5385177.219975264, 'kernel': 'rbf', 'gamma': 4.025316189742124e-06}
0.680 (+/-0.293) for {'C': 5385177.219975264, 'kernel': 'rbf', 'gamma': 4.0401733537300044e-06}
0.663 (+/-0.315) for {'C': 5385177.219975264, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.696 (+/-0.302) for {'C': 5405053.537086678, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.696 (+/-0.303) for {'C': 5405053.537086678, 'kernel': 'rbf', 'gamma': 3.922834625395207e-06}
0.696 (+/-0.304) for {'C': 5405053.537086678, 'kernel': 'rbf', 'gamma': 3.937313537008499e-06}
0.696 (+/-0.303) for {'C': 5405053.537086678, 'kernel': 'rbf', 'gamma': 3.951845889284363e-06}
0.679 (+/-0.290) for {'C': 5405053.537086678, 'kernel': 'rbf', 'gamma': 3.966431879468585e-06}
0.696 (+/-0.304) for {'C': 5405053.537086678, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.695 (+/-0.301) for {'C': 5405053.537086678, 'kernel': 'rbf', 'gamma': 3.995765566188054e-06}
0.679 (+/-0.292) for {'C': 5405053.537086678, 'kernel': 'rbf', 'gamma': 4.01051366086576e-06}
0.696 (+/-0.305) for {'C': 5405053.537086678, 'kernel': 'rbf', 'gamma': 4.025316189742124e-06}
0.680 (+/-0.293) for {'C': 5405053.537086678, 'kernel': 'rbf', 'gamma': 4.0401733537300044e-06}
0.663 (+/-0.315) for {'C': 5405053.537086678, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.696 (+/-0.302) for {'C': 5425003.216311495, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.696 (+/-0.303) for {'C': 5425003.216311495, 'kernel': 'rbf', 'gamma': 3.922834625395207e-06}
0.696 (+/-0.304) for {'C': 5425003.216311495, 'kernel': 'rbf', 'gamma': 3.937313537008499e-06}
0.696 (+/-0.303) for {'C': 5425003.216311495, 'kernel': 'rbf', 'gamma': 3.951845889284363e-06}
0.679 (+/-0.290) for {'C': 5425003.216311495, 'kernel': 'rbf', 'gamma': 3.966431879468585e-06}
0.696 (+/-0.304) for {'C': 5425003.216311495, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.695 (+/-0.301) for {'C': 5425003.216311495, 'kernel': 'rbf', 'gamma': 3.995765566188054e-06}
0.679 (+/-0.292) for {'C': 5425003.216311495, 'kernel': 'rbf', 'gamma': 4.01051366086576e-06}
0.696 (+/-0.305) for {'C': 5425003.216311495, 'kernel': 'rbf', 'gamma': 4.025316189742124e-06}
0.680 (+/-0.293) for {'C': 5425003.216311495, 'kernel': 'rbf', 'gamma': 4.0401733537300044e-06}
0.663 (+/-0.315) for {'C': 5425003.216311495, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.696 (+/-0.302) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.696 (+/-0.303) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.922834625395207e-06}
0.696 (+/-0.304) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.937313537008499e-06}
0.696 (+/-0.303) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.951845889284363e-06}
0.679 (+/-0.290) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.966431879468585e-06}
0.696 (+/-0.304) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.695 (+/-0.301) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.995765566188054e-06}
0.679 (+/-0.292) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 4.01051366086576e-06}
0.696 (+/-0.305) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 4.025316189742124e-06}
0.680 (+/-0.294) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 4.0401733537300044e-06}
0.663 (+/-0.315) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.696 (+/-0.302) for {'C': 5465123.745198711, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.696 (+/-0.303) for {'C': 5465123.745198711, 'kernel': 'rbf', 'gamma': 3.922834625395207e-06}
0.696 (+/-0.304) for {'C': 5465123.745198711, 'kernel': 'rbf', 'gamma': 3.937313537008499e-06}
0.696 (+/-0.303) for {'C': 5465123.745198711, 'kernel': 'rbf', 'gamma': 3.951845889284363e-06}
0.695 (+/-0.303) for {'C': 5465123.745198711, 'kernel': 'rbf', 'gamma': 3.966431879468585e-06}
0.696 (+/-0.304) for {'C': 5465123.745198711, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.695 (+/-0.301) for {'C': 5465123.745198711, 'kernel': 'rbf', 'gamma': 3.995765566188054e-06}
0.679 (+/-0.292) for {'C': 5465123.745198711, 'kernel': 'rbf', 'gamma': 4.01051366086576e-06}
0.696 (+/-0.305) for {'C': 5465123.745198711, 'kernel': 'rbf', 'gamma': 4.025316189742124e-06}
0.680 (+/-0.294) for {'C': 5465123.745198711, 'kernel': 'rbf', 'gamma': 4.0401733537300044e-06}
0.663 (+/-0.315) for {'C': 5465123.745198711, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.696 (+/-0.303) for {'C': 5485295.139412026, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.696 (+/-0.303) for {'C': 5485295.139412026, 'kernel': 'rbf', 'gamma': 3.922834625395207e-06}
0.696 (+/-0.304) for {'C': 5485295.139412026, 'kernel': 'rbf', 'gamma': 3.937313537008499e-06}
0.696 (+/-0.303) for {'C': 5485295.139412026, 'kernel': 'rbf', 'gamma': 3.951845889284363e-06}
0.679 (+/-0.290) for {'C': 5485295.139412026, 'kernel': 'rbf', 'gamma': 3.966431879468585e-06}
0.696 (+/-0.304) for {'C': 5485295.139412026, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.695 (+/-0.302) for {'C': 5485295.139412026, 'kernel': 'rbf', 'gamma': 3.995765566188054e-06}
0.679 (+/-0.292) for {'C': 5485295.139412026, 'kernel': 'rbf', 'gamma': 4.01051366086576e-06}
0.696 (+/-0.305) for {'C': 5485295.139412026, 'kernel': 'rbf', 'gamma': 4.025316189742124e-06}
0.680 (+/-0.293) for {'C': 5485295.139412026, 'kernel': 'rbf', 'gamma': 4.0401733537300044e-06}
0.663 (+/-0.315) for {'C': 5485295.139412026, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.696 (+/-0.303) for {'C': 5505540.984847944, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.696 (+/-0.303) for {'C': 5505540.984847944, 'kernel': 'rbf', 'gamma': 3.922834625395207e-06}
0.696 (+/-0.303) for {'C': 5505540.984847944, 'kernel': 'rbf', 'gamma': 3.937313537008499e-06}
0.696 (+/-0.303) for {'C': 5505540.984847944, 'kernel': 'rbf', 'gamma': 3.951845889284363e-06}
0.679 (+/-0.290) for {'C': 5505540.984847944, 'kernel': 'rbf', 'gamma': 3.966431879468585e-06}
0.696 (+/-0.304) for {'C': 5505540.984847944, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.695 (+/-0.302) for {'C': 5505540.984847944, 'kernel': 'rbf', 'gamma': 3.995765566188054e-06}
0.679 (+/-0.292) for {'C': 5505540.984847944, 'kernel': 'rbf', 'gamma': 4.01051366086576e-06}
0.696 (+/-0.305) for {'C': 5505540.984847944, 'kernel': 'rbf', 'gamma': 4.025316189742124e-06}
0.680 (+/-0.294) for {'C': 5505540.984847944, 'kernel': 'rbf', 'gamma': 4.0401733537300044e-06}
0.663 (+/-0.315) for {'C': 5505540.984847944, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.696 (+/-0.302) for {'C': 5525861.556300772, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.696 (+/-0.303) for {'C': 5525861.556300772, 'kernel': 'rbf', 'gamma': 3.922834625395207e-06}
0.696 (+/-0.303) for {'C': 5525861.556300772, 'kernel': 'rbf', 'gamma': 3.937313537008499e-06}
0.696 (+/-0.303) for {'C': 5525861.556300772, 'kernel': 'rbf', 'gamma': 3.951845889284363e-06}
0.679 (+/-0.290) for {'C': 5525861.556300772, 'kernel': 'rbf', 'gamma': 3.966431879468585e-06}
0.696 (+/-0.304) for {'C': 5525861.556300772, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.695 (+/-0.302) for {'C': 5525861.556300772, 'kernel': 'rbf', 'gamma': 3.995765566188054e-06}
0.679 (+/-0.292) for {'C': 5525861.556300772, 'kernel': 'rbf', 'gamma': 4.01051366086576e-06}
0.696 (+/-0.305) for {'C': 5525861.556300772, 'kernel': 'rbf', 'gamma': 4.025316189742124e-06}
0.680 (+/-0.293) for {'C': 5525861.556300772, 'kernel': 'rbf', 'gamma': 4.0401733537300044e-06}
0.663 (+/-0.315) for {'C': 5525861.556300772, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.696 (+/-0.302) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.696 (+/-0.303) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.922834625395207e-06}
0.696 (+/-0.304) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.937313537008499e-06}
0.696 (+/-0.303) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.951845889284363e-06}
0.679 (+/-0.290) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.966431879468585e-06}
0.696 (+/-0.304) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.695 (+/-0.303) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.995765566188054e-06}
0.679 (+/-0.292) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 4.01051366086576e-06}
0.696 (+/-0.305) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 4.025316189742124e-06}
0.680 (+/-0.293) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 4.0401733537300044e-06}
0.663 (+/-0.315) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.699 (+/-0.307) for {'C': 1.0, 'kernel': 'linear'}
0.696 (+/-0.304) for {'C': 10.0, 'kernel': 'linear'}
0.663 (+/-0.314) for {'C': 100.0, 'kernel': 'linear'}
0.660 (+/-0.316) for {'C': 1000.0, 'kernel': 'linear'}
0.634 (+/-0.321) for {'C': 10000.0, 'kernel': 'linear'}
0.632 (+/-0.323) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.20 0.17 0.18 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.99 0.99 629
本轮grid search结果,得到最好的参数选择是: {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6985561464259213
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.598 (+/-0.165) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.595 (+/-0.167) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.922834625395207e-06}
0.610 (+/-0.151) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.937313537008499e-06}
0.609 (+/-0.179) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.951845889284363e-06}
0.605 (+/-0.180) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.966431879468585e-06}
0.594 (+/-0.159) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.597 (+/-0.158) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.995765566188054e-06}
0.606 (+/-0.146) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 4.01051366086576e-06}
0.616 (+/-0.167) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 4.025316189742124e-06}
0.602 (+/-0.183) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 4.0401733537300044e-06}
0.581 (+/-0.171) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.598 (+/-0.165) for {'C': 5365373.99519851, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.595 (+/-0.167) for {'C': 5365373.99519851, 'kernel': 'rbf', 'gamma': 3.922834625395207e-06}
0.608 (+/-0.148) for {'C': 5365373.99519851, 'kernel': 'rbf', 'gamma': 3.937313537008499e-06}
0.609 (+/-0.179) for {'C': 5365373.99519851, 'kernel': 'rbf', 'gamma': 3.951845889284363e-06}
0.598 (+/-0.175) for {'C': 5365373.99519851, 'kernel': 'rbf', 'gamma': 3.966431879468585e-06}
0.600 (+/-0.148) for {'C': 5365373.99519851, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.597 (+/-0.158) for {'C': 5365373.99519851, 'kernel': 'rbf', 'gamma': 3.995765566188054e-06}
0.600 (+/-0.140) for {'C': 5365373.99519851, 'kernel': 'rbf', 'gamma': 4.01051366086576e-06}
0.616 (+/-0.167) for {'C': 5365373.99519851, 'kernel': 'rbf', 'gamma': 4.025316189742124e-06}
0.602 (+/-0.183) for {'C': 5365373.99519851, 'kernel': 'rbf', 'gamma': 4.0401733537300044e-06}
0.581 (+/-0.171) for {'C': 5365373.99519851, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.598 (+/-0.165) for {'C': 5385177.219975264, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.595 (+/-0.167) for {'C': 5385177.219975264, 'kernel': 'rbf', 'gamma': 3.922834625395207e-06}
0.608 (+/-0.148) for {'C': 5385177.219975264, 'kernel': 'rbf', 'gamma': 3.937313537008499e-06}
0.609 (+/-0.179) for {'C': 5385177.219975264, 'kernel': 'rbf', 'gamma': 3.951845889284363e-06}
0.597 (+/-0.175) for {'C': 5385177.219975264, 'kernel': 'rbf', 'gamma': 3.966431879468585e-06}
0.600 (+/-0.148) for {'C': 5385177.219975264, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.597 (+/-0.158) for {'C': 5385177.219975264, 'kernel': 'rbf', 'gamma': 3.995765566188054e-06}
0.600 (+/-0.140) for {'C': 5385177.219975264, 'kernel': 'rbf', 'gamma': 4.01051366086576e-06}
0.616 (+/-0.167) for {'C': 5385177.219975264, 'kernel': 'rbf', 'gamma': 4.025316189742124e-06}
0.602 (+/-0.183) for {'C': 5385177.219975264, 'kernel': 'rbf', 'gamma': 4.0401733537300044e-06}
0.581 (+/-0.171) for {'C': 5385177.219975264, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.598 (+/-0.165) for {'C': 5405053.537086678, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.595 (+/-0.167) for {'C': 5405053.537086678, 'kernel': 'rbf', 'gamma': 3.922834625395207e-06}
0.608 (+/-0.148) for {'C': 5405053.537086678, 'kernel': 'rbf', 'gamma': 3.937313537008499e-06}
0.609 (+/-0.179) for {'C': 5405053.537086678, 'kernel': 'rbf', 'gamma': 3.951845889284363e-06}
0.597 (+/-0.175) for {'C': 5405053.537086678, 'kernel': 'rbf', 'gamma': 3.966431879468585e-06}
0.600 (+/-0.148) for {'C': 5405053.537086678, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.597 (+/-0.158) for {'C': 5405053.537086678, 'kernel': 'rbf', 'gamma': 3.995765566188054e-06}
0.600 (+/-0.140) for {'C': 5405053.537086678, 'kernel': 'rbf', 'gamma': 4.01051366086576e-06}
0.616 (+/-0.167) for {'C': 5405053.537086678, 'kernel': 'rbf', 'gamma': 4.025316189742124e-06}
0.602 (+/-0.183) for {'C': 5405053.537086678, 'kernel': 'rbf', 'gamma': 4.0401733537300044e-06}
0.581 (+/-0.171) for {'C': 5405053.537086678, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.598 (+/-0.165) for {'C': 5425003.216311495, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.595 (+/-0.167) for {'C': 5425003.216311495, 'kernel': 'rbf', 'gamma': 3.922834625395207e-06}
0.608 (+/-0.148) for {'C': 5425003.216311495, 'kernel': 'rbf', 'gamma': 3.937313537008499e-06}
0.609 (+/-0.179) for {'C': 5425003.216311495, 'kernel': 'rbf', 'gamma': 3.951845889284363e-06}
0.598 (+/-0.175) for {'C': 5425003.216311495, 'kernel': 'rbf', 'gamma': 3.966431879468585e-06}
0.600 (+/-0.148) for {'C': 5425003.216311495, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.597 (+/-0.158) for {'C': 5425003.216311495, 'kernel': 'rbf', 'gamma': 3.995765566188054e-06}
0.600 (+/-0.140) for {'C': 5425003.216311495, 'kernel': 'rbf', 'gamma': 4.01051366086576e-06}
0.616 (+/-0.167) for {'C': 5425003.216311495, 'kernel': 'rbf', 'gamma': 4.025316189742124e-06}
0.602 (+/-0.183) for {'C': 5425003.216311495, 'kernel': 'rbf', 'gamma': 4.0401733537300044e-06}
0.581 (+/-0.171) for {'C': 5425003.216311495, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.598 (+/-0.165) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.595 (+/-0.167) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.922834625395207e-06}
0.610 (+/-0.151) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.937313537008499e-06}
0.609 (+/-0.179) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.951845889284363e-06}
0.597 (+/-0.175) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.966431879468585e-06}
0.600 (+/-0.148) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.597 (+/-0.158) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.995765566188054e-06}
0.599 (+/-0.141) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 4.01051366086576e-06}
0.616 (+/-0.167) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 4.025316189742124e-06}
0.602 (+/-0.183) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 4.0401733537300044e-06}
0.581 (+/-0.171) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.598 (+/-0.165) for {'C': 5465123.745198711, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.595 (+/-0.167) for {'C': 5465123.745198711, 'kernel': 'rbf', 'gamma': 3.922834625395207e-06}
0.610 (+/-0.151) for {'C': 5465123.745198711, 'kernel': 'rbf', 'gamma': 3.937313537008499e-06}
0.609 (+/-0.179) for {'C': 5465123.745198711, 'kernel': 'rbf', 'gamma': 3.951845889284363e-06}
0.604 (+/-0.180) for {'C': 5465123.745198711, 'kernel': 'rbf', 'gamma': 3.966431879468585e-06}
0.600 (+/-0.148) for {'C': 5465123.745198711, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.597 (+/-0.158) for {'C': 5465123.745198711, 'kernel': 'rbf', 'gamma': 3.995765566188054e-06}
0.599 (+/-0.141) for {'C': 5465123.745198711, 'kernel': 'rbf', 'gamma': 4.01051366086576e-06}
0.616 (+/-0.167) for {'C': 5465123.745198711, 'kernel': 'rbf', 'gamma': 4.025316189742124e-06}
0.602 (+/-0.183) for {'C': 5465123.745198711, 'kernel': 'rbf', 'gamma': 4.0401733537300044e-06}
0.581 (+/-0.171) for {'C': 5465123.745198711, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.598 (+/-0.165) for {'C': 5485295.139412026, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.595 (+/-0.167) for {'C': 5485295.139412026, 'kernel': 'rbf', 'gamma': 3.922834625395207e-06}
0.608 (+/-0.148) for {'C': 5485295.139412026, 'kernel': 'rbf', 'gamma': 3.937313537008499e-06}
0.609 (+/-0.179) for {'C': 5485295.139412026, 'kernel': 'rbf', 'gamma': 3.951845889284363e-06}
0.597 (+/-0.175) for {'C': 5485295.139412026, 'kernel': 'rbf', 'gamma': 3.966431879468585e-06}
0.600 (+/-0.148) for {'C': 5485295.139412026, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.597 (+/-0.158) for {'C': 5485295.139412026, 'kernel': 'rbf', 'gamma': 3.995765566188054e-06}
0.599 (+/-0.141) for {'C': 5485295.139412026, 'kernel': 'rbf', 'gamma': 4.01051366086576e-06}
0.616 (+/-0.167) for {'C': 5485295.139412026, 'kernel': 'rbf', 'gamma': 4.025316189742124e-06}
0.602 (+/-0.183) for {'C': 5485295.139412026, 'kernel': 'rbf', 'gamma': 4.0401733537300044e-06}
0.581 (+/-0.171) for {'C': 5485295.139412026, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.598 (+/-0.165) for {'C': 5505540.984847944, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.594 (+/-0.168) for {'C': 5505540.984847944, 'kernel': 'rbf', 'gamma': 3.922834625395207e-06}
0.608 (+/-0.148) for {'C': 5505540.984847944, 'kernel': 'rbf', 'gamma': 3.937313537008499e-06}
0.609 (+/-0.179) for {'C': 5505540.984847944, 'kernel': 'rbf', 'gamma': 3.951845889284363e-06}
0.597 (+/-0.175) for {'C': 5505540.984847944, 'kernel': 'rbf', 'gamma': 3.966431879468585e-06}
0.600 (+/-0.148) for {'C': 5505540.984847944, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.597 (+/-0.158) for {'C': 5505540.984847944, 'kernel': 'rbf', 'gamma': 3.995765566188054e-06}
0.599 (+/-0.141) for {'C': 5505540.984847944, 'kernel': 'rbf', 'gamma': 4.01051366086576e-06}
0.616 (+/-0.167) for {'C': 5505540.984847944, 'kernel': 'rbf', 'gamma': 4.025316189742124e-06}
0.602 (+/-0.183) for {'C': 5505540.984847944, 'kernel': 'rbf', 'gamma': 4.0401733537300044e-06}
0.581 (+/-0.171) for {'C': 5505540.984847944, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.598 (+/-0.165) for {'C': 5525861.556300772, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.595 (+/-0.167) for {'C': 5525861.556300772, 'kernel': 'rbf', 'gamma': 3.922834625395207e-06}
0.608 (+/-0.148) for {'C': 5525861.556300772, 'kernel': 'rbf', 'gamma': 3.937313537008499e-06}
0.609 (+/-0.179) for {'C': 5525861.556300772, 'kernel': 'rbf', 'gamma': 3.951845889284363e-06}
0.597 (+/-0.175) for {'C': 5525861.556300772, 'kernel': 'rbf', 'gamma': 3.966431879468585e-06}
0.599 (+/-0.149) for {'C': 5525861.556300772, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.597 (+/-0.158) for {'C': 5525861.556300772, 'kernel': 'rbf', 'gamma': 3.995765566188054e-06}
0.599 (+/-0.141) for {'C': 5525861.556300772, 'kernel': 'rbf', 'gamma': 4.01051366086576e-06}
0.616 (+/-0.167) for {'C': 5525861.556300772, 'kernel': 'rbf', 'gamma': 4.025316189742124e-06}
0.602 (+/-0.183) for {'C': 5525861.556300772, 'kernel': 'rbf', 'gamma': 4.0401733537300044e-06}
0.581 (+/-0.171) for {'C': 5525861.556300772, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.598 (+/-0.165) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.594 (+/-0.168) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.922834625395207e-06}
0.608 (+/-0.148) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.937313537008499e-06}
0.609 (+/-0.179) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.951845889284363e-06}
0.603 (+/-0.165) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.966431879468585e-06}
0.599 (+/-0.149) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.599 (+/-0.158) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.995765566188054e-06}
0.599 (+/-0.141) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 4.01051366086576e-06}
0.616 (+/-0.167) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 4.025316189742124e-06}
0.602 (+/-0.183) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 4.0401733537300044e-06}
0.581 (+/-0.171) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.798 (+/-0.437) for {'C': 0.1, 'kernel': 'linear'}
0.635 (+/-0.198) for {'C': 1.0, 'kernel': 'linear'}
0.586 (+/-0.152) for {'C': 10.0, 'kernel': 'linear'}
0.587 (+/-0.195) for {'C': 100.0, 'kernel': 'linear'}
0.589 (+/-0.193) for {'C': 1000.0, 'kernel': 'linear'}
0.582 (+/-0.201) for {'C': 10000.0, 'kernel': 'linear'}
0.582 (+/-0.201) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.718 (+/-0.349) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.693 (+/-0.303) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.922834625395207e-06}
0.743 (+/-0.318) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.937313537008499e-06}
0.718 (+/-0.353) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.951845889284363e-06}
0.718 (+/-0.352) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.966431879468585e-06}
0.717 (+/-0.350) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.719 (+/-0.353) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 3.995765566188054e-06}
0.744 (+/-0.319) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 4.01051366086576e-06}
0.744 (+/-0.318) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 4.025316189742124e-06}
0.676 (+/-0.291) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 4.0401733537300044e-06}
0.660 (+/-0.310) for {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.718 (+/-0.349) for {'C': 5365373.99519851, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.693 (+/-0.303) for {'C': 5365373.99519851, 'kernel': 'rbf', 'gamma': 3.922834625395207e-06}
0.743 (+/-0.317) for {'C': 5365373.99519851, 'kernel': 'rbf', 'gamma': 3.937313537008499e-06}
0.718 (+/-0.353) for {'C': 5365373.99519851, 'kernel': 'rbf', 'gamma': 3.951845889284363e-06}
0.702 (+/-0.345) for {'C': 5365373.99519851, 'kernel': 'rbf', 'gamma': 3.966431879468585e-06}
0.742 (+/-0.315) for {'C': 5365373.99519851, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.719 (+/-0.353) for {'C': 5365373.99519851, 'kernel': 'rbf', 'gamma': 3.995765566188054e-06}
0.727 (+/-0.317) for {'C': 5365373.99519851, 'kernel': 'rbf', 'gamma': 4.01051366086576e-06}
0.744 (+/-0.318) for {'C': 5365373.99519851, 'kernel': 'rbf', 'gamma': 4.025316189742124e-06}
0.676 (+/-0.291) for {'C': 5365373.99519851, 'kernel': 'rbf', 'gamma': 4.0401733537300044e-06}
0.660 (+/-0.310) for {'C': 5365373.99519851, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.718 (+/-0.349) for {'C': 5385177.219975264, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.693 (+/-0.303) for {'C': 5385177.219975264, 'kernel': 'rbf', 'gamma': 3.922834625395207e-06}
0.743 (+/-0.317) for {'C': 5385177.219975264, 'kernel': 'rbf', 'gamma': 3.937313537008499e-06}
0.718 (+/-0.353) for {'C': 5385177.219975264, 'kernel': 'rbf', 'gamma': 3.951845889284363e-06}
0.701 (+/-0.345) for {'C': 5385177.219975264, 'kernel': 'rbf', 'gamma': 3.966431879468585e-06}
0.742 (+/-0.315) for {'C': 5385177.219975264, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.719 (+/-0.353) for {'C': 5385177.219975264, 'kernel': 'rbf', 'gamma': 3.995765566188054e-06}
0.727 (+/-0.317) for {'C': 5385177.219975264, 'kernel': 'rbf', 'gamma': 4.01051366086576e-06}
0.744 (+/-0.318) for {'C': 5385177.219975264, 'kernel': 'rbf', 'gamma': 4.025316189742124e-06}
0.676 (+/-0.291) for {'C': 5385177.219975264, 'kernel': 'rbf', 'gamma': 4.0401733537300044e-06}
0.660 (+/-0.310) for {'C': 5385177.219975264, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.718 (+/-0.349) for {'C': 5405053.537086678, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.693 (+/-0.303) for {'C': 5405053.537086678, 'kernel': 'rbf', 'gamma': 3.922834625395207e-06}
0.743 (+/-0.317) for {'C': 5405053.537086678, 'kernel': 'rbf', 'gamma': 3.937313537008499e-06}
0.718 (+/-0.353) for {'C': 5405053.537086678, 'kernel': 'rbf', 'gamma': 3.951845889284363e-06}
0.701 (+/-0.345) for {'C': 5405053.537086678, 'kernel': 'rbf', 'gamma': 3.966431879468585e-06}
0.742 (+/-0.315) for {'C': 5405053.537086678, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.719 (+/-0.353) for {'C': 5405053.537086678, 'kernel': 'rbf', 'gamma': 3.995765566188054e-06}
0.727 (+/-0.317) for {'C': 5405053.537086678, 'kernel': 'rbf', 'gamma': 4.01051366086576e-06}
0.744 (+/-0.318) for {'C': 5405053.537086678, 'kernel': 'rbf', 'gamma': 4.025316189742124e-06}
0.676 (+/-0.291) for {'C': 5405053.537086678, 'kernel': 'rbf', 'gamma': 4.0401733537300044e-06}
0.660 (+/-0.310) for {'C': 5405053.537086678, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.718 (+/-0.349) for {'C': 5425003.216311495, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.693 (+/-0.303) for {'C': 5425003.216311495, 'kernel': 'rbf', 'gamma': 3.922834625395207e-06}
0.743 (+/-0.317) for {'C': 5425003.216311495, 'kernel': 'rbf', 'gamma': 3.937313537008499e-06}
0.718 (+/-0.353) for {'C': 5425003.216311495, 'kernel': 'rbf', 'gamma': 3.951845889284363e-06}
0.702 (+/-0.345) for {'C': 5425003.216311495, 'kernel': 'rbf', 'gamma': 3.966431879468585e-06}
0.742 (+/-0.315) for {'C': 5425003.216311495, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.719 (+/-0.353) for {'C': 5425003.216311495, 'kernel': 'rbf', 'gamma': 3.995765566188054e-06}
0.727 (+/-0.317) for {'C': 5425003.216311495, 'kernel': 'rbf', 'gamma': 4.01051366086576e-06}
0.744 (+/-0.318) for {'C': 5425003.216311495, 'kernel': 'rbf', 'gamma': 4.025316189742124e-06}
0.676 (+/-0.291) for {'C': 5425003.216311495, 'kernel': 'rbf', 'gamma': 4.0401733537300044e-06}
0.660 (+/-0.310) for {'C': 5425003.216311495, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.718 (+/-0.349) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.693 (+/-0.303) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.922834625395207e-06}
0.743 (+/-0.318) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.937313537008499e-06}
0.718 (+/-0.353) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.951845889284363e-06}
0.701 (+/-0.345) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.966431879468585e-06}
0.742 (+/-0.315) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.718 (+/-0.353) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 3.995765566188054e-06}
0.727 (+/-0.317) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 4.01051366086576e-06}
0.744 (+/-0.318) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 4.025316189742124e-06}
0.676 (+/-0.291) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 4.0401733537300044e-06}
0.660 (+/-0.310) for {'C': 5445026.528424197, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.718 (+/-0.349) for {'C': 5465123.745198711, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.693 (+/-0.303) for {'C': 5465123.745198711, 'kernel': 'rbf', 'gamma': 3.922834625395207e-06}
0.743 (+/-0.318) for {'C': 5465123.745198711, 'kernel': 'rbf', 'gamma': 3.937313537008499e-06}
0.718 (+/-0.353) for {'C': 5465123.745198711, 'kernel': 'rbf', 'gamma': 3.951845889284363e-06}
0.718 (+/-0.351) for {'C': 5465123.745198711, 'kernel': 'rbf', 'gamma': 3.966431879468585e-06}
0.742 (+/-0.315) for {'C': 5465123.745198711, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.718 (+/-0.353) for {'C': 5465123.745198711, 'kernel': 'rbf', 'gamma': 3.995765566188054e-06}
0.727 (+/-0.317) for {'C': 5465123.745198711, 'kernel': 'rbf', 'gamma': 4.01051366086576e-06}
0.744 (+/-0.318) for {'C': 5465123.745198711, 'kernel': 'rbf', 'gamma': 4.025316189742124e-06}
0.676 (+/-0.291) for {'C': 5465123.745198711, 'kernel': 'rbf', 'gamma': 4.0401733537300044e-06}
0.660 (+/-0.310) for {'C': 5465123.745198711, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.718 (+/-0.349) for {'C': 5485295.139412026, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.693 (+/-0.303) for {'C': 5485295.139412026, 'kernel': 'rbf', 'gamma': 3.922834625395207e-06}
0.743 (+/-0.317) for {'C': 5485295.139412026, 'kernel': 'rbf', 'gamma': 3.937313537008499e-06}
0.718 (+/-0.353) for {'C': 5485295.139412026, 'kernel': 'rbf', 'gamma': 3.951845889284363e-06}
0.701 (+/-0.344) for {'C': 5485295.139412026, 'kernel': 'rbf', 'gamma': 3.966431879468585e-06}
0.742 (+/-0.315) for {'C': 5485295.139412026, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.718 (+/-0.353) for {'C': 5485295.139412026, 'kernel': 'rbf', 'gamma': 3.995765566188054e-06}
0.727 (+/-0.317) for {'C': 5485295.139412026, 'kernel': 'rbf', 'gamma': 4.01051366086576e-06}
0.744 (+/-0.318) for {'C': 5485295.139412026, 'kernel': 'rbf', 'gamma': 4.025316189742124e-06}
0.676 (+/-0.291) for {'C': 5485295.139412026, 'kernel': 'rbf', 'gamma': 4.0401733537300044e-06}
0.660 (+/-0.310) for {'C': 5485295.139412026, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.718 (+/-0.349) for {'C': 5505540.984847944, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.692 (+/-0.301) for {'C': 5505540.984847944, 'kernel': 'rbf', 'gamma': 3.922834625395207e-06}
0.743 (+/-0.317) for {'C': 5505540.984847944, 'kernel': 'rbf', 'gamma': 3.937313537008499e-06}
0.718 (+/-0.353) for {'C': 5505540.984847944, 'kernel': 'rbf', 'gamma': 3.951845889284363e-06}
0.701 (+/-0.344) for {'C': 5505540.984847944, 'kernel': 'rbf', 'gamma': 3.966431879468585e-06}
0.742 (+/-0.315) for {'C': 5505540.984847944, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.718 (+/-0.353) for {'C': 5505540.984847944, 'kernel': 'rbf', 'gamma': 3.995765566188054e-06}
0.727 (+/-0.317) for {'C': 5505540.984847944, 'kernel': 'rbf', 'gamma': 4.01051366086576e-06}
0.744 (+/-0.318) for {'C': 5505540.984847944, 'kernel': 'rbf', 'gamma': 4.025316189742124e-06}
0.676 (+/-0.291) for {'C': 5505540.984847944, 'kernel': 'rbf', 'gamma': 4.0401733537300044e-06}
0.660 (+/-0.310) for {'C': 5505540.984847944, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.718 (+/-0.349) for {'C': 5525861.556300772, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.693 (+/-0.303) for {'C': 5525861.556300772, 'kernel': 'rbf', 'gamma': 3.922834625395207e-06}
0.743 (+/-0.317) for {'C': 5525861.556300772, 'kernel': 'rbf', 'gamma': 3.937313537008499e-06}
0.718 (+/-0.353) for {'C': 5525861.556300772, 'kernel': 'rbf', 'gamma': 3.951845889284363e-06}
0.701 (+/-0.344) for {'C': 5525861.556300772, 'kernel': 'rbf', 'gamma': 3.966431879468585e-06}
0.742 (+/-0.315) for {'C': 5525861.556300772, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.718 (+/-0.353) for {'C': 5525861.556300772, 'kernel': 'rbf', 'gamma': 3.995765566188054e-06}
0.727 (+/-0.317) for {'C': 5525861.556300772, 'kernel': 'rbf', 'gamma': 4.01051366086576e-06}
0.744 (+/-0.318) for {'C': 5525861.556300772, 'kernel': 'rbf', 'gamma': 4.025316189742124e-06}
0.676 (+/-0.291) for {'C': 5525861.556300772, 'kernel': 'rbf', 'gamma': 4.0401733537300044e-06}
0.660 (+/-0.310) for {'C': 5525861.556300772, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.718 (+/-0.349) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.908408957924017e-06}
0.692 (+/-0.302) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.922834625395207e-06}
0.743 (+/-0.317) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.937313537008499e-06}
0.718 (+/-0.353) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.951845889284363e-06}
0.726 (+/-0.314) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.966431879468585e-06}
0.742 (+/-0.315) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.718 (+/-0.353) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 3.995765566188054e-06}
0.727 (+/-0.317) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 4.01051366086576e-06}
0.744 (+/-0.318) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 4.025316189742124e-06}
0.676 (+/-0.291) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 4.0401733537300044e-06}
0.660 (+/-0.310) for {'C': 5546257.129579104, 'kernel': 'rbf', 'gamma': 4.055085354483842e-06}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.700 (+/-0.309) for {'C': 0.1, 'kernel': 'linear'}
0.698 (+/-0.305) for {'C': 1.0, 'kernel': 'linear'}
0.694 (+/-0.301) for {'C': 10.0, 'kernel': 'linear'}
0.657 (+/-0.313) for {'C': 100.0, 'kernel': 'linear'}
0.659 (+/-0.314) for {'C': 1000.0, 'kernel': 'linear'}
0.633 (+/-0.321) for {'C': 10000.0, 'kernel': 'linear'}
0.632 (+/-0.323) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.06 0.17 0.08 6
1 0.99 0.97 0.98 623
avg / total 0.98 0.97 0.97 629
本轮grid search结果,得到最好的参数选择是: {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 4.01051366086576e-06}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.7437474235303818
发现最优参数C为原先的最大/最小值,直接重新设置超参。
第2轮loop中,tunemodel函数得到的最优参数是: ([{'C': array([5.34564359e+01, 5.34564359e+02, 5.34564359e+03, 5.34564359e+04,
5.34564359e+05, 5.34564359e+06, 5.34564359e+07, 5.34564359e+08,
5.34564359e+09, 5.34564359e+10, 5.34564359e+11]), 'kernel': ['rbf'], 'gamma': array([3.99576557e-06, 3.99871084e-06, 4.00165828e-06, 4.00460790e-06,
4.00755969e-06, 4.01051366e-06, 4.01346981e-06, 4.01642813e-06,
4.01938863e-06, 4.02235132e-06, 4.02531619e-06])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}], {'C': 5345643.593969704, 'kernel': 'rbf', 'gamma': 4.01051366086576e-06}, 0.7437474235303818)
这是第2次迭代微调C和gamma。
第2次迭代,得到delta: [9.93829345e+04 2.94419553e-08]
SVC模型clf_op_iter的参数是: {'kernel': 'rbf', 'decision_function_shape': 'ovr', 'class_weight': {-1: 15}, 'tol': 0.001, 'gamma': 4.01051366086576e-06, 'random_state': 0, 'cache_size': 200, 'verbose': False, 'degree': 3, 'C': 5345643.593969704, 'probability': False, 'shrinking': True, 'coef0': 0.0, 'max_iter': -1}
训练模型SVC对训练样本的预测准确率: 0.9518072289156626
测试集中,预测为舞弊样本的有: (array([ 0, 1, 2, ..., 1254, 1255, 1256], dtype=int64),)
测试集中,实际为舞弊样本的有: (array([1246, 1247, 1248, 1249, 1250, 1251, 1252, 1253, 1254, 1255, 1256],
dtype=int64),)
预测的舞弊样本数目: 1076
训练模型SVC对测试样本的预测准确率: 0.23387668320340185
以上是第40次特征筛选。
第40次特征筛选,AUC值是: 0.5267765941923246
X_train_iter_svc.shape is: (1257, 12)
X_test_iter_svc.shape is: (1257, 12)
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.722 (+/-0.473) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.609 (+/-0.309) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.660 (+/-0.219) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.612 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.698 (+/-0.306) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.17 0.17 0.17 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6982356336054085
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.660 (+/-0.219) for {'C': 1.0, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.585 (+/-0.188) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.14 0.17 0.15 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.698 (+/-0.306) for {'C': 1.0, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.659 (+/-0.313) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.14 0.17 0.15 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6982356336054085
RBF的粗grid search最佳超参: {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001} RBF的粗grid search最高分值: 0.6982356336054085
linear的粗grid search最佳超参: {'C': 1.0, 'kernel': 'linear'} linear的粗grid search最高分值: 0.6982356336054085
粗grid search得到的parameter是:
[{'C': array([1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02, 1.e+03, 1.e+04, 1.e+05,
1.e+06, 1.e+07, 1.e+08]), 'kernel': ['rbf'], 'gamma': array([1.e-08, 1.e-07, 1.e-06, 1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01,
1.e+00, 1.e+01, 1.e+02])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.848 (+/-0.459) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.848 (+/-0.459) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.685 (+/-0.297) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.308) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.848 (+/-0.459) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.660 (+/-0.219) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.644 (+/-0.285) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.608 (+/-0.309) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.848 (+/-0.459) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.660 (+/-0.219) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.611 (+/-0.184) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.597 (+/-0.315) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.609 (+/-0.309) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.848 (+/-0.459) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.660 (+/-0.219) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.608 (+/-0.185) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.593 (+/-0.188) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.604 (+/-0.309) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.609 (+/-0.309) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.848 (+/-0.459) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.660 (+/-0.219) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.611 (+/-0.185) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.599 (+/-0.192) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.572 (+/-0.201) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.606 (+/-0.308) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.609 (+/-0.309) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.748 (+/-0.396) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.651 (+/-0.211) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.610 (+/-0.183) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.591 (+/-0.189) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.587 (+/-0.189) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.576 (+/-0.196) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.606 (+/-0.308) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.609 (+/-0.309) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.848 (+/-0.460) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.703 (+/-0.418) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.622 (+/-0.291) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.595 (+/-0.191) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.581 (+/-0.187) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.578 (+/-0.195) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.578 (+/-0.195) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.606 (+/-0.308) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.609 (+/-0.309) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.793 (+/-0.440) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.690 (+/-0.435) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.614 (+/-0.292) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.592 (+/-0.192) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.580 (+/-0.188) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.578 (+/-0.196) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.578 (+/-0.195) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.606 (+/-0.308) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.609 (+/-0.309) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.685 (+/-0.297) for {'C': 1.0, 'kernel': 'linear'}
0.607 (+/-0.182) for {'C': 10.0, 'kernel': 'linear'}
0.608 (+/-0.197) for {'C': 100.0, 'kernel': 'linear'}
0.589 (+/-0.189) for {'C': 1000.0, 'kernel': 'linear'}
0.585 (+/-0.189) for {'C': 10000.0, 'kernel': 'linear'}
0.580 (+/-0.195) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [1]
检查交叉验证集中测试集标签是否有预测不到的值: {-1}
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 1.00 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.658 (+/-0.217) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.004) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.658 (+/-0.217) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.698 (+/-0.306) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.007) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.658 (+/-0.217) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.306) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.304) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.611 (+/-0.240) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.497 (+/-0.006) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.658 (+/-0.217) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.698 (+/-0.306) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.696 (+/-0.304) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.587 (+/-0.231) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.612 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.497 (+/-0.006) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.658 (+/-0.217) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.698 (+/-0.306) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.696 (+/-0.304) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.662 (+/-0.315) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.611 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.497 (+/-0.006) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.658 (+/-0.217) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.698 (+/-0.306) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.696 (+/-0.304) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.662 (+/-0.316) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.585 (+/-0.235) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.497 (+/-0.006) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.699 (+/-0.308) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.698 (+/-0.306) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.696 (+/-0.302) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.661 (+/-0.315) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.660 (+/-0.314) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.610 (+/-0.239) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.239) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.237) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.497 (+/-0.006) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.683 (+/-0.296) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.683 (+/-0.258) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.695 (+/-0.303) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.662 (+/-0.311) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.658 (+/-0.311) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.632 (+/-0.323) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.611 (+/-0.239) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.239) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.237) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.497 (+/-0.006) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.699 (+/-0.307) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.633 (+/-0.237) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.694 (+/-0.301) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.660 (+/-0.312) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.657 (+/-0.310) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.631 (+/-0.324) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.611 (+/-0.239) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.239) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.237) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.497 (+/-0.006) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.698 (+/-0.306) for {'C': 1.0, 'kernel': 'linear'}
0.696 (+/-0.303) for {'C': 10.0, 'kernel': 'linear'}
0.663 (+/-0.314) for {'C': 100.0, 'kernel': 'linear'}
0.660 (+/-0.314) for {'C': 1000.0, 'kernel': 'linear'}
0.659 (+/-0.312) for {'C': 10000.0, 'kernel': 'linear'}
0.634 (+/-0.325) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6991982026547944
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.669 (+/-0.296) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.667 (+/-0.292) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.656 (+/-0.384) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.642 (+/-0.204) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.633 (+/-0.197) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.593 (+/-0.296) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.642 (+/-0.204) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.624 (+/-0.186) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.618 (+/-0.278) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.608 (+/-0.309) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.642 (+/-0.204) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.624 (+/-0.186) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.585 (+/-0.153) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.601 (+/-0.311) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.609 (+/-0.309) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.642 (+/-0.204) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.624 (+/-0.186) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.582 (+/-0.153) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.580 (+/-0.188) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.604 (+/-0.309) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.609 (+/-0.309) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.747 (+/-0.502) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.642 (+/-0.204) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.631 (+/-0.197) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.585 (+/-0.155) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.581 (+/-0.191) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.576 (+/-0.197) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.606 (+/-0.308) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.609 (+/-0.309) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.647 (+/-0.460) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.656 (+/-0.231) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.606 (+/-0.180) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.590 (+/-0.158) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.578 (+/-0.189) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.585 (+/-0.189) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.576 (+/-0.196) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.606 (+/-0.308) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.609 (+/-0.309) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.848 (+/-0.460) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.542 (+/-0.143) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.599 (+/-0.282) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.591 (+/-0.188) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.580 (+/-0.188) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.578 (+/-0.195) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.578 (+/-0.195) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.606 (+/-0.308) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.609 (+/-0.309) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.793 (+/-0.440) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.553 (+/-0.296) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.608 (+/-0.286) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.591 (+/-0.188) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.580 (+/-0.188) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.578 (+/-0.196) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.578 (+/-0.195) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.606 (+/-0.308) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.609 (+/-0.309) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.798 (+/-0.437) for {'C': 0.1, 'kernel': 'linear'}
0.660 (+/-0.219) for {'C': 1.0, 'kernel': 'linear'}
0.584 (+/-0.147) for {'C': 10.0, 'kernel': 'linear'}
0.587 (+/-0.195) for {'C': 100.0, 'kernel': 'linear'}
0.585 (+/-0.188) for {'C': 1000.0, 'kernel': 'linear'}
0.585 (+/-0.189) for {'C': 10000.0, 'kernel': 'linear'}
0.580 (+/-0.195) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.682 (+/-0.294) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.698 (+/-0.306) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.494 (+/-0.012) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.306) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.305) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.611 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.007) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.698 (+/-0.306) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.304) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.694 (+/-0.305) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.611 (+/-0.240) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.497 (+/-0.006) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.698 (+/-0.306) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.304) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.693 (+/-0.304) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.608 (+/-0.241) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.612 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.497 (+/-0.006) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.698 (+/-0.306) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.304) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.692 (+/-0.304) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.657 (+/-0.312) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.611 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.497 (+/-0.006) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.617 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.698 (+/-0.306) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.697 (+/-0.304) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.693 (+/-0.304) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.657 (+/-0.310) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.608 (+/-0.242) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.239) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.497 (+/-0.006) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.575 (+/-0.229) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.698 (+/-0.308) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.696 (+/-0.300) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.693 (+/-0.304) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.656 (+/-0.310) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.659 (+/-0.313) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.610 (+/-0.239) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.239) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.237) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.497 (+/-0.006) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.700 (+/-0.309) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.660 (+/-0.256) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.693 (+/-0.301) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.705 (+/-0.341) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.657 (+/-0.310) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.632 (+/-0.323) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.611 (+/-0.239) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.239) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.237) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.497 (+/-0.006) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.699 (+/-0.307) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.607 (+/-0.162) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.694 (+/-0.303) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.705 (+/-0.340) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.657 (+/-0.310) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.631 (+/-0.324) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.611 (+/-0.239) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.239) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.237) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.497 (+/-0.006) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.700 (+/-0.309) for {'C': 0.1, 'kernel': 'linear'}
0.698 (+/-0.306) for {'C': 1.0, 'kernel': 'linear'}
0.694 (+/-0.301) for {'C': 10.0, 'kernel': 'linear'}
0.657 (+/-0.313) for {'C': 100.0, 'kernel': 'linear'}
0.659 (+/-0.313) for {'C': 1000.0, 'kernel': 'linear'}
0.659 (+/-0.313) for {'C': 10000.0, 'kernel': 'linear'}
0.634 (+/-0.325) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.07 0.33 0.12 6
1 0.99 0.96 0.98 623
avg / total 0.98 0.95 0.97 629
本轮grid search结果,得到最好的参数选择是: {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.705286400362767
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.651 (+/-0.211) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.693 (+/-0.350) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.634 (+/-0.193) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.620 (+/-0.183) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.601 (+/-0.176) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.610 (+/-0.183) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.598 (+/-0.159) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.617 (+/-0.187) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.601 (+/-0.198) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.595 (+/-0.188) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.591 (+/-0.189) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.664 (+/-0.291) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.639 (+/-0.202) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.625 (+/-0.184) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.595 (+/-0.160) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.594 (+/-0.157) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.605 (+/-0.165) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.616 (+/-0.182) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.588 (+/-0.185) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.594 (+/-0.190) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.590 (+/-0.188) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.591 (+/-0.189) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.658 (+/-0.290) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.661 (+/-0.293) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.602 (+/-0.182) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.593 (+/-0.155) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.625 (+/-0.211) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.611 (+/-0.183) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.606 (+/-0.176) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.589 (+/-0.187) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.596 (+/-0.193) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.589 (+/-0.189) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.589 (+/-0.191) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.649 (+/-0.286) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.640 (+/-0.288) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.597 (+/-0.161) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.599 (+/-0.180) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.614 (+/-0.179) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.600 (+/-0.197) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.597 (+/-0.195) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.588 (+/-0.187) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.595 (+/-0.194) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.585 (+/-0.189) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.584 (+/-0.187) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.638 (+/-0.292) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.625 (+/-0.289) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.601 (+/-0.180) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.600 (+/-0.180) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.619 (+/-0.181) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.595 (+/-0.191) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.591 (+/-0.189) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.588 (+/-0.187) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.589 (+/-0.191) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.587 (+/-0.191) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.583 (+/-0.187) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.621 (+/-0.291) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.622 (+/-0.288) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.603 (+/-0.182) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.599 (+/-0.175) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.599 (+/-0.190) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.595 (+/-0.191) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.590 (+/-0.194) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.587 (+/-0.187) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.586 (+/-0.190) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.585 (+/-0.190) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.581 (+/-0.187) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.616 (+/-0.292) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.593 (+/-0.175) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.611 (+/-0.185) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.587 (+/-0.186) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.598 (+/-0.194) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.595 (+/-0.191) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.581 (+/-0.172) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.580 (+/-0.187) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.582 (+/-0.188) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.584 (+/-0.190) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.580 (+/-0.188) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.616 (+/-0.292) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.594 (+/-0.176) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.603 (+/-0.177) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.587 (+/-0.186) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.600 (+/-0.197) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.593 (+/-0.191) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.581 (+/-0.172) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.580 (+/-0.188) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.586 (+/-0.186) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.583 (+/-0.189) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.580 (+/-0.188) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.615 (+/-0.293) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.592 (+/-0.176) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.594 (+/-0.188) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.586 (+/-0.186) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.587 (+/-0.172) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.593 (+/-0.191) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.582 (+/-0.173) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.580 (+/-0.188) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.585 (+/-0.186) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.583 (+/-0.190) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.579 (+/-0.189) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.614 (+/-0.293) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.595 (+/-0.175) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.605 (+/-0.209) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.588 (+/-0.186) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.587 (+/-0.172) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.592 (+/-0.192) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.582 (+/-0.173) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.579 (+/-0.188) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.585 (+/-0.186) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.583 (+/-0.190) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.580 (+/-0.188) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.614 (+/-0.292) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.595 (+/-0.174) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.608 (+/-0.209) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.588 (+/-0.186) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.586 (+/-0.173) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.592 (+/-0.192) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.581 (+/-0.172) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.579 (+/-0.188) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.584 (+/-0.186) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.583 (+/-0.190) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.580 (+/-0.188) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.685 (+/-0.297) for {'C': 1.0, 'kernel': 'linear'}
0.607 (+/-0.182) for {'C': 10.0, 'kernel': 'linear'}
0.608 (+/-0.197) for {'C': 100.0, 'kernel': 'linear'}
0.589 (+/-0.189) for {'C': 1000.0, 'kernel': 'linear'}
0.585 (+/-0.189) for {'C': 10000.0, 'kernel': 'linear'}
0.580 (+/-0.195) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.698 (+/-0.306) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.698 (+/-0.306) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.698 (+/-0.306) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.697 (+/-0.304) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.695 (+/-0.302) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.696 (+/-0.302) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.696 (+/-0.303) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.696 (+/-0.304) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.663 (+/-0.315) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.662 (+/-0.314) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.661 (+/-0.315) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.698 (+/-0.306) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.698 (+/-0.306) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.697 (+/-0.304) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.695 (+/-0.302) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.695 (+/-0.303) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.696 (+/-0.300) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.680 (+/-0.290) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.662 (+/-0.311) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.662 (+/-0.315) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.661 (+/-0.315) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.661 (+/-0.315) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.698 (+/-0.305) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.698 (+/-0.306) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.695 (+/-0.302) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.695 (+/-0.303) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.697 (+/-0.307) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.679 (+/-0.289) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.679 (+/-0.292) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.661 (+/-0.312) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.661 (+/-0.316) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.660 (+/-0.315) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.660 (+/-0.315) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.697 (+/-0.304) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.696 (+/-0.303) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.695 (+/-0.303) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.695 (+/-0.302) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.680 (+/-0.294) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.662 (+/-0.308) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.663 (+/-0.312) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.660 (+/-0.313) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.660 (+/-0.318) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.659 (+/-0.314) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.659 (+/-0.313) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.696 (+/-0.303) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.696 (+/-0.302) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.679 (+/-0.291) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.695 (+/-0.301) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.680 (+/-0.295) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.662 (+/-0.310) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.661 (+/-0.314) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.660 (+/-0.313) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.660 (+/-0.317) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.659 (+/-0.314) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.659 (+/-0.312) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.695 (+/-0.302) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.695 (+/-0.301) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.679 (+/-0.292) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.679 (+/-0.290) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.662 (+/-0.315) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.662 (+/-0.311) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.660 (+/-0.313) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.660 (+/-0.312) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.659 (+/-0.315) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.658 (+/-0.314) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.658 (+/-0.311) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.694 (+/-0.301) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.695 (+/-0.299) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.696 (+/-0.304) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.662 (+/-0.311) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.662 (+/-0.315) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.661 (+/-0.312) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.659 (+/-0.312) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.659 (+/-0.312) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.658 (+/-0.314) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.658 (+/-0.312) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.658 (+/-0.309) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.694 (+/-0.301) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.694 (+/-0.300) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.679 (+/-0.292) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.662 (+/-0.311) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.662 (+/-0.315) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.661 (+/-0.312) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.659 (+/-0.313) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.658 (+/-0.312) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.683 (+/-0.367) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.659 (+/-0.311) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.657 (+/-0.309) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.694 (+/-0.301) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.694 (+/-0.300) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.663 (+/-0.313) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.662 (+/-0.311) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.662 (+/-0.315) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.661 (+/-0.312) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.659 (+/-0.314) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.658 (+/-0.312) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.683 (+/-0.367) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.658 (+/-0.310) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.657 (+/-0.308) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.694 (+/-0.301) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.695 (+/-0.301) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.663 (+/-0.315) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.662 (+/-0.312) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.661 (+/-0.315) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.660 (+/-0.312) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.659 (+/-0.315) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.658 (+/-0.311) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.683 (+/-0.366) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.658 (+/-0.310) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.658 (+/-0.309) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.694 (+/-0.301) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.695 (+/-0.301) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.663 (+/-0.315) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.662 (+/-0.312) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.661 (+/-0.315) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.660 (+/-0.312) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.658 (+/-0.314) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.658 (+/-0.310) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.683 (+/-0.365) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.658 (+/-0.310) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.657 (+/-0.310) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.698 (+/-0.306) for {'C': 1.0, 'kernel': 'linear'}
0.696 (+/-0.303) for {'C': 10.0, 'kernel': 'linear'}
0.663 (+/-0.314) for {'C': 100.0, 'kernel': 'linear'}
0.660 (+/-0.314) for {'C': 1000.0, 'kernel': 'linear'}
0.659 (+/-0.312) for {'C': 10000.0, 'kernel': 'linear'}
0.634 (+/-0.325) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.20 0.17 0.18 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.99 0.99 629
本轮grid search结果,得到最好的参数选择是: {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6983958900156649
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.606 (+/-0.180) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.645 (+/-0.363) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.584 (+/-0.153) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.576 (+/-0.141) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.585 (+/-0.145) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.590 (+/-0.158) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.585 (+/-0.162) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.602 (+/-0.189) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.583 (+/-0.190) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.582 (+/-0.190) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.578 (+/-0.189) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.610 (+/-0.294) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.582 (+/-0.178) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.581 (+/-0.147) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.581 (+/-0.144) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.581 (+/-0.146) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.597 (+/-0.160) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.601 (+/-0.184) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.581 (+/-0.187) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.583 (+/-0.190) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.581 (+/-0.191) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.582 (+/-0.190) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.591 (+/-0.281) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.586 (+/-0.177) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.587 (+/-0.154) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.586 (+/-0.152) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.591 (+/-0.153) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.600 (+/-0.182) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.593 (+/-0.184) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.581 (+/-0.191) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.583 (+/-0.190) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.582 (+/-0.190) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.582 (+/-0.190) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.594 (+/-0.282) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.579 (+/-0.143) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.591 (+/-0.156) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.596 (+/-0.181) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.598 (+/-0.178) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.593 (+/-0.187) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.585 (+/-0.196) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.581 (+/-0.191) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.583 (+/-0.190) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.579 (+/-0.189) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.580 (+/-0.188) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.594 (+/-0.281) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.582 (+/-0.146) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.594 (+/-0.175) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.596 (+/-0.182) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.606 (+/-0.175) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.592 (+/-0.188) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.581 (+/-0.191) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.581 (+/-0.191) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.580 (+/-0.188) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.581 (+/-0.191) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.581 (+/-0.187) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.599 (+/-0.282) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.584 (+/-0.144) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.591 (+/-0.175) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.591 (+/-0.177) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.589 (+/-0.182) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.591 (+/-0.188) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.585 (+/-0.196) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.581 (+/-0.191) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.580 (+/-0.189) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.582 (+/-0.190) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.580 (+/-0.188) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.602 (+/-0.281) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.580 (+/-0.144) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.599 (+/-0.181) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.578 (+/-0.188) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.599 (+/-0.178) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.591 (+/-0.188) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.576 (+/-0.174) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.577 (+/-0.189) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.579 (+/-0.189) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.581 (+/-0.191) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.580 (+/-0.188) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.608 (+/-0.287) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.582 (+/-0.145) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.594 (+/-0.175) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.578 (+/-0.188) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.599 (+/-0.178) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.592 (+/-0.188) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.576 (+/-0.174) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.577 (+/-0.189) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.582 (+/-0.187) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.582 (+/-0.190) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.580 (+/-0.188) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.608 (+/-0.287) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.580 (+/-0.144) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.586 (+/-0.185) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.578 (+/-0.188) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.586 (+/-0.150) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.592 (+/-0.188) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.576 (+/-0.174) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.577 (+/-0.189) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.583 (+/-0.186) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.582 (+/-0.190) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.579 (+/-0.189) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.609 (+/-0.286) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.583 (+/-0.144) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.585 (+/-0.185) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.577 (+/-0.188) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.586 (+/-0.150) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.591 (+/-0.188) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.576 (+/-0.174) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.577 (+/-0.189) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.583 (+/-0.186) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.582 (+/-0.190) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.580 (+/-0.188) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.608 (+/-0.286) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.582 (+/-0.144) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.586 (+/-0.186) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.577 (+/-0.188) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.586 (+/-0.150) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.591 (+/-0.188) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.576 (+/-0.174) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.577 (+/-0.189) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.583 (+/-0.186) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.582 (+/-0.190) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.580 (+/-0.188) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.798 (+/-0.437) for {'C': 0.1, 'kernel': 'linear'}
0.660 (+/-0.219) for {'C': 1.0, 'kernel': 'linear'}
0.584 (+/-0.147) for {'C': 10.0, 'kernel': 'linear'}
0.587 (+/-0.195) for {'C': 100.0, 'kernel': 'linear'}
0.585 (+/-0.188) for {'C': 1000.0, 'kernel': 'linear'}
0.585 (+/-0.189) for {'C': 10000.0, 'kernel': 'linear'}
0.580 (+/-0.195) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.696 (+/-0.300) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.695 (+/-0.299) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.694 (+/-0.299) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.693 (+/-0.300) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.694 (+/-0.304) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.693 (+/-0.304) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.691 (+/-0.304) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.715 (+/-0.351) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.657 (+/-0.314) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.657 (+/-0.311) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.656 (+/-0.310) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.693 (+/-0.296) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.692 (+/-0.296) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.694 (+/-0.300) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.694 (+/-0.301) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.693 (+/-0.305) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.717 (+/-0.353) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.698 (+/-0.344) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.681 (+/-0.363) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.657 (+/-0.314) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.656 (+/-0.311) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.657 (+/-0.310) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.691 (+/-0.297) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.693 (+/-0.298) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.694 (+/-0.304) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.693 (+/-0.303) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.717 (+/-0.353) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.699 (+/-0.344) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.671 (+/-0.292) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.656 (+/-0.310) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.657 (+/-0.313) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.657 (+/-0.311) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.657 (+/-0.310) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.692 (+/-0.297) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.694 (+/-0.299) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.694 (+/-0.303) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.693 (+/-0.304) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.699 (+/-0.345) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.707 (+/-0.340) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.655 (+/-0.314) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.656 (+/-0.311) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.657 (+/-0.314) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.657 (+/-0.310) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.658 (+/-0.310) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.692 (+/-0.300) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.694 (+/-0.301) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.678 (+/-0.291) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.693 (+/-0.303) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.724 (+/-0.313) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.706 (+/-0.341) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.655 (+/-0.313) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.656 (+/-0.310) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.657 (+/-0.313) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.657 (+/-0.311) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.658 (+/-0.311) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.693 (+/-0.301) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.694 (+/-0.301) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.677 (+/-0.292) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.676 (+/-0.289) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.706 (+/-0.342) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.705 (+/-0.341) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.655 (+/-0.313) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.656 (+/-0.310) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.657 (+/-0.312) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.657 (+/-0.312) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.657 (+/-0.310) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.693 (+/-0.302) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.694 (+/-0.300) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.694 (+/-0.305) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.658 (+/-0.310) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.731 (+/-0.311) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.705 (+/-0.340) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.655 (+/-0.311) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.656 (+/-0.310) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.656 (+/-0.312) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.657 (+/-0.311) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.658 (+/-0.309) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.694 (+/-0.303) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.694 (+/-0.301) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.677 (+/-0.293) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.658 (+/-0.310) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.731 (+/-0.311) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.705 (+/-0.341) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.655 (+/-0.311) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.656 (+/-0.309) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.682 (+/-0.365) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.657 (+/-0.310) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.657 (+/-0.309) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.693 (+/-0.304) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.694 (+/-0.301) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.661 (+/-0.313) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.658 (+/-0.310) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.730 (+/-0.310) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.705 (+/-0.340) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.655 (+/-0.311) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.656 (+/-0.309) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.682 (+/-0.366) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.657 (+/-0.311) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.657 (+/-0.308) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.694 (+/-0.303) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.694 (+/-0.301) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.661 (+/-0.312) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.658 (+/-0.310) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.730 (+/-0.310) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.705 (+/-0.340) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.655 (+/-0.311) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.656 (+/-0.309) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.682 (+/-0.366) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.657 (+/-0.311) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.658 (+/-0.309) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.694 (+/-0.303) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.694 (+/-0.302) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.661 (+/-0.313) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.658 (+/-0.310) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.730 (+/-0.310) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.705 (+/-0.340) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.655 (+/-0.311) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.657 (+/-0.309) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.682 (+/-0.366) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.657 (+/-0.311) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.657 (+/-0.310) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.700 (+/-0.309) for {'C': 0.1, 'kernel': 'linear'}
0.698 (+/-0.306) for {'C': 1.0, 'kernel': 'linear'}
0.694 (+/-0.301) for {'C': 10.0, 'kernel': 'linear'}
0.657 (+/-0.313) for {'C': 100.0, 'kernel': 'linear'}
0.659 (+/-0.313) for {'C': 1000.0, 'kernel': 'linear'}
0.659 (+/-0.313) for {'C': 10000.0, 'kernel': 'linear'}
0.634 (+/-0.325) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.05 0.17 0.07 6
1 0.99 0.97 0.98 623
avg / total 0.98 0.96 0.97 629
本轮grid search结果,得到最好的参数选择是: {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.7309279412977162
循环迭代之前,delta is: [5.84893192e+06 3.69042656e-06]
执行tunemodel()函数前,使用的grid search parameter是:
[{'C': array([ 9999999.99999999, 10964781.96143185, 12022644.34617414,
13182567.38556406, 14454397.70745927, 15848931.92461113,
17378008.28749376, 19054607.17963249, 20892961.30854038,
22908676.52767773, 25118864.31509581]), 'kernel': ['rbf'], 'gamma': array([3.98107171e-06, 4.36515832e-06, 4.78630092e-06, 5.24807460e-06,
5.75439937e-06, 6.30957344e-06, 6.91830971e-06, 7.58577575e-06,
8.31763771e-06, 9.12010839e-06, 1.00000000e-05])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.599 (+/-0.175) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.589 (+/-0.176) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.598 (+/-0.183) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.585 (+/-0.170) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.603 (+/-0.201) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.599 (+/-0.190) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.585 (+/-0.186) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.587 (+/-0.187) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.577 (+/-0.162) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.582 (+/-0.184) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.595 (+/-0.191) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.601 (+/-0.175) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.583 (+/-0.170) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.598 (+/-0.183) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.581 (+/-0.164) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.603 (+/-0.201) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.604 (+/-0.193) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.584 (+/-0.186) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.587 (+/-0.187) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.577 (+/-0.162) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.585 (+/-0.186) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.595 (+/-0.191) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.601 (+/-0.175) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.583 (+/-0.170) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.598 (+/-0.183) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.581 (+/-0.164) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.603 (+/-0.201) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.601 (+/-0.193) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.584 (+/-0.186) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.587 (+/-0.187) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.575 (+/-0.162) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.585 (+/-0.186) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.595 (+/-0.191) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.588 (+/-0.186) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.583 (+/-0.170) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.598 (+/-0.183) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.581 (+/-0.164) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.603 (+/-0.201) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.599 (+/-0.193) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.588 (+/-0.191) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.586 (+/-0.188) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.576 (+/-0.162) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.585 (+/-0.186) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.595 (+/-0.191) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.588 (+/-0.186) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.579 (+/-0.164) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.598 (+/-0.183) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.580 (+/-0.164) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.603 (+/-0.201) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.599 (+/-0.193) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.588 (+/-0.191) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.586 (+/-0.188) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.575 (+/-0.162) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.585 (+/-0.186) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.595 (+/-0.191) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.587 (+/-0.186) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.583 (+/-0.170) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.603 (+/-0.196) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.577 (+/-0.162) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.603 (+/-0.201) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.598 (+/-0.194) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.588 (+/-0.191) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.585 (+/-0.188) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.575 (+/-0.162) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.582 (+/-0.184) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.595 (+/-0.191) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.587 (+/-0.186) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.583 (+/-0.170) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.603 (+/-0.196) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.577 (+/-0.162) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.605 (+/-0.201) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.598 (+/-0.194) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.584 (+/-0.186) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.585 (+/-0.188) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.573 (+/-0.161) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.582 (+/-0.184) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.595 (+/-0.191) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.587 (+/-0.186) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.583 (+/-0.170) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.603 (+/-0.196) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.581 (+/-0.168) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.605 (+/-0.201) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.598 (+/-0.194) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.584 (+/-0.186) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.585 (+/-0.188) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.573 (+/-0.161) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.583 (+/-0.184) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.595 (+/-0.191) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.587 (+/-0.186) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.583 (+/-0.170) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.603 (+/-0.196) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.581 (+/-0.168) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.605 (+/-0.201) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.601 (+/-0.197) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.584 (+/-0.186) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.577 (+/-0.164) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.573 (+/-0.161) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.583 (+/-0.184) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.593 (+/-0.191) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.587 (+/-0.186) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.579 (+/-0.164) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.601 (+/-0.195) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.577 (+/-0.162) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.605 (+/-0.201) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.601 (+/-0.197) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.584 (+/-0.186) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.576 (+/-0.165) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.572 (+/-0.162) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.583 (+/-0.184) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.593 (+/-0.191) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.587 (+/-0.186) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.579 (+/-0.164) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.601 (+/-0.195) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.577 (+/-0.162) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.605 (+/-0.201) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.600 (+/-0.197) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.584 (+/-0.186) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.576 (+/-0.165) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.572 (+/-0.162) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.583 (+/-0.184) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.593 (+/-0.191) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.685 (+/-0.297) for {'C': 1.0, 'kernel': 'linear'}
0.607 (+/-0.182) for {'C': 10.0, 'kernel': 'linear'}
0.608 (+/-0.197) for {'C': 100.0, 'kernel': 'linear'}
0.589 (+/-0.189) for {'C': 1000.0, 'kernel': 'linear'}
0.585 (+/-0.189) for {'C': 10000.0, 'kernel': 'linear'}
0.580 (+/-0.195) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.679 (+/-0.290) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.662 (+/-0.308) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.663 (+/-0.315) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.662 (+/-0.308) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.662 (+/-0.316) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.662 (+/-0.315) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.661 (+/-0.309) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.661 (+/-0.311) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.661 (+/-0.311) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.661 (+/-0.310) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.662 (+/-0.311) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.679 (+/-0.290) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.662 (+/-0.307) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.663 (+/-0.315) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.662 (+/-0.309) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.662 (+/-0.316) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.662 (+/-0.315) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.660 (+/-0.309) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.660 (+/-0.311) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.660 (+/-0.310) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.661 (+/-0.310) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.662 (+/-0.312) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.679 (+/-0.290) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.662 (+/-0.307) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.663 (+/-0.315) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.662 (+/-0.309) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.663 (+/-0.315) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.662 (+/-0.315) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.660 (+/-0.309) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.661 (+/-0.311) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.660 (+/-0.308) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.661 (+/-0.310) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.661 (+/-0.312) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.662 (+/-0.311) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.662 (+/-0.307) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.663 (+/-0.315) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.662 (+/-0.309) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.663 (+/-0.315) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.662 (+/-0.315) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.661 (+/-0.309) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.660 (+/-0.311) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.660 (+/-0.309) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.661 (+/-0.310) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.661 (+/-0.312) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.662 (+/-0.311) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.662 (+/-0.307) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.663 (+/-0.315) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.661 (+/-0.308) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.663 (+/-0.315) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.662 (+/-0.314) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.661 (+/-0.309) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.660 (+/-0.311) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.660 (+/-0.309) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.660 (+/-0.311) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.661 (+/-0.312) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.662 (+/-0.311) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.662 (+/-0.307) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.663 (+/-0.316) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.661 (+/-0.308) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.662 (+/-0.316) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.662 (+/-0.315) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.661 (+/-0.309) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.660 (+/-0.311) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.660 (+/-0.309) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.660 (+/-0.311) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.661 (+/-0.312) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.662 (+/-0.311) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.662 (+/-0.307) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.663 (+/-0.316) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.661 (+/-0.308) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.662 (+/-0.316) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.662 (+/-0.314) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.660 (+/-0.309) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.660 (+/-0.311) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.659 (+/-0.308) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.660 (+/-0.311) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.661 (+/-0.312) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.662 (+/-0.311) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.662 (+/-0.307) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.663 (+/-0.316) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.661 (+/-0.308) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.662 (+/-0.316) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.662 (+/-0.315) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.660 (+/-0.309) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.660 (+/-0.311) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.659 (+/-0.307) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.660 (+/-0.311) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.661 (+/-0.312) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.662 (+/-0.311) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.662 (+/-0.307) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.663 (+/-0.316) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.661 (+/-0.308) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.662 (+/-0.316) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.662 (+/-0.316) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.660 (+/-0.309) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.659 (+/-0.311) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.659 (+/-0.307) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.660 (+/-0.312) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.661 (+/-0.312) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.662 (+/-0.311) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.661 (+/-0.307) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.663 (+/-0.316) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.661 (+/-0.308) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.662 (+/-0.316) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.662 (+/-0.316) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.660 (+/-0.309) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.659 (+/-0.311) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.659 (+/-0.307) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.660 (+/-0.311) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.661 (+/-0.312) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.662 (+/-0.311) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.661 (+/-0.307) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.663 (+/-0.316) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.661 (+/-0.308) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.662 (+/-0.316) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.662 (+/-0.315) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.660 (+/-0.309) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.659 (+/-0.311) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.659 (+/-0.307) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.660 (+/-0.311) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.661 (+/-0.312) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.698 (+/-0.306) for {'C': 1.0, 'kernel': 'linear'}
0.696 (+/-0.303) for {'C': 10.0, 'kernel': 'linear'}
0.663 (+/-0.314) for {'C': 100.0, 'kernel': 'linear'}
0.660 (+/-0.314) for {'C': 1000.0, 'kernel': 'linear'}
0.659 (+/-0.312) for {'C': 10000.0, 'kernel': 'linear'}
0.634 (+/-0.325) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.20 0.17 0.18 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.99 0.99 629
本轮grid search结果,得到最好的参数选择是: {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6983958900156649
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.591 (+/-0.177) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.580 (+/-0.169) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.573 (+/-0.163) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.575 (+/-0.171) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.585 (+/-0.192) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.589 (+/-0.182) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.585 (+/-0.182) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.581 (+/-0.191) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.569 (+/-0.165) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.578 (+/-0.186) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.591 (+/-0.188) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.593 (+/-0.179) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.575 (+/-0.162) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.572 (+/-0.163) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.571 (+/-0.164) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.585 (+/-0.192) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.593 (+/-0.187) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.585 (+/-0.182) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.580 (+/-0.192) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.569 (+/-0.165) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.581 (+/-0.187) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.591 (+/-0.188) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.592 (+/-0.180) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.575 (+/-0.162) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.572 (+/-0.163) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.571 (+/-0.164) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.585 (+/-0.192) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.599 (+/-0.178) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.585 (+/-0.182) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.580 (+/-0.191) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.569 (+/-0.165) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.581 (+/-0.187) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.591 (+/-0.189) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.579 (+/-0.187) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.575 (+/-0.162) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.572 (+/-0.163) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.571 (+/-0.164) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.585 (+/-0.192) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.593 (+/-0.187) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.589 (+/-0.187) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.580 (+/-0.192) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.569 (+/-0.165) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.581 (+/-0.187) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.591 (+/-0.188) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.579 (+/-0.188) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.575 (+/-0.162) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.572 (+/-0.163) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.569 (+/-0.163) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.585 (+/-0.192) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.599 (+/-0.178) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.589 (+/-0.187) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.580 (+/-0.192) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.570 (+/-0.165) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.581 (+/-0.187) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.591 (+/-0.188) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.578 (+/-0.188) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.575 (+/-0.162) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.572 (+/-0.163) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.569 (+/-0.163) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.585 (+/-0.192) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.599 (+/-0.178) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.589 (+/-0.187) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.583 (+/-0.189) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.570 (+/-0.165) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.578 (+/-0.186) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.591 (+/-0.188) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.578 (+/-0.188) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.575 (+/-0.162) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.572 (+/-0.163) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.569 (+/-0.163) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.585 (+/-0.192) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.599 (+/-0.178) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.585 (+/-0.182) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.583 (+/-0.189) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.568 (+/-0.164) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.579 (+/-0.186) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.591 (+/-0.188) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.578 (+/-0.188) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.575 (+/-0.162) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.572 (+/-0.163) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.573 (+/-0.170) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.585 (+/-0.192) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.599 (+/-0.178) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.585 (+/-0.182) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.583 (+/-0.189) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.568 (+/-0.164) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.579 (+/-0.186) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.591 (+/-0.188) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.578 (+/-0.188) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.574 (+/-0.162) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.572 (+/-0.163) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.574 (+/-0.170) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.585 (+/-0.192) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.599 (+/-0.178) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.585 (+/-0.182) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.575 (+/-0.165) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.568 (+/-0.164) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.579 (+/-0.186) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.591 (+/-0.188) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.578 (+/-0.188) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.574 (+/-0.162) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.569 (+/-0.161) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.569 (+/-0.163) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.585 (+/-0.192) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.599 (+/-0.178) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.585 (+/-0.182) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.575 (+/-0.165) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.568 (+/-0.164) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.579 (+/-0.186) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.591 (+/-0.188) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.578 (+/-0.188) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.574 (+/-0.162) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.569 (+/-0.161) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.569 (+/-0.163) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.585 (+/-0.192) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.599 (+/-0.178) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.585 (+/-0.182) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.575 (+/-0.165) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.568 (+/-0.164) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.579 (+/-0.186) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.592 (+/-0.188) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.798 (+/-0.437) for {'C': 0.1, 'kernel': 'linear'}
0.660 (+/-0.219) for {'C': 1.0, 'kernel': 'linear'}
0.584 (+/-0.147) for {'C': 10.0, 'kernel': 'linear'}
0.587 (+/-0.195) for {'C': 100.0, 'kernel': 'linear'}
0.585 (+/-0.188) for {'C': 1000.0, 'kernel': 'linear'}
0.585 (+/-0.189) for {'C': 10000.0, 'kernel': 'linear'}
0.580 (+/-0.195) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.676 (+/-0.289) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.683 (+/-0.365) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.657 (+/-0.313) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.657 (+/-0.311) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.680 (+/-0.369) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.706 (+/-0.342) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.705 (+/-0.343) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.655 (+/-0.311) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.654 (+/-0.310) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.680 (+/-0.363) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.705 (+/-0.341) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.676 (+/-0.290) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.682 (+/-0.366) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.657 (+/-0.312) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.657 (+/-0.310) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.680 (+/-0.370) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.705 (+/-0.344) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.705 (+/-0.343) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.655 (+/-0.310) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.654 (+/-0.311) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.681 (+/-0.363) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.705 (+/-0.341) for {'C': 10964781.961431852, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.675 (+/-0.291) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.682 (+/-0.366) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.657 (+/-0.312) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.657 (+/-0.311) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.680 (+/-0.370) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.730 (+/-0.313) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.705 (+/-0.343) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.655 (+/-0.311) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.653 (+/-0.312) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.681 (+/-0.363) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.705 (+/-0.340) for {'C': 12022644.346174138, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.658 (+/-0.310) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.682 (+/-0.366) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.657 (+/-0.312) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.656 (+/-0.310) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.680 (+/-0.370) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.706 (+/-0.342) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.705 (+/-0.343) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.654 (+/-0.311) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.654 (+/-0.312) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.681 (+/-0.363) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.705 (+/-0.340) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.658 (+/-0.310) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.682 (+/-0.366) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.657 (+/-0.312) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.656 (+/-0.310) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.679 (+/-0.371) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.731 (+/-0.310) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.705 (+/-0.343) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.654 (+/-0.311) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.654 (+/-0.313) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.681 (+/-0.363) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.705 (+/-0.340) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.658 (+/-0.310) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.682 (+/-0.367) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.657 (+/-0.313) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.656 (+/-0.311) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.679 (+/-0.370) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.731 (+/-0.311) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.705 (+/-0.343) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.679 (+/-0.365) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.654 (+/-0.312) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.680 (+/-0.363) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.705 (+/-0.340) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.658 (+/-0.310) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.682 (+/-0.367) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.657 (+/-0.313) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.656 (+/-0.311) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.679 (+/-0.370) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.731 (+/-0.311) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.704 (+/-0.343) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.679 (+/-0.365) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.653 (+/-0.312) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.680 (+/-0.364) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.705 (+/-0.340) for {'C': 17378008.28749376, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.658 (+/-0.310) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.682 (+/-0.367) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.657 (+/-0.313) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.656 (+/-0.310) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.679 (+/-0.370) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.731 (+/-0.311) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.704 (+/-0.343) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.680 (+/-0.365) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.653 (+/-0.312) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.681 (+/-0.364) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.705 (+/-0.340) for {'C': 19054607.179632492, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.658 (+/-0.310) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.682 (+/-0.366) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.657 (+/-0.312) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.656 (+/-0.311) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.679 (+/-0.370) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.731 (+/-0.311) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.704 (+/-0.343) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.680 (+/-0.364) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.653 (+/-0.312) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.681 (+/-0.364) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.705 (+/-0.340) for {'C': 20892961.308540378, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.658 (+/-0.310) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.682 (+/-0.366) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.657 (+/-0.312) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.656 (+/-0.310) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.679 (+/-0.371) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.731 (+/-0.311) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.704 (+/-0.343) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.680 (+/-0.364) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.653 (+/-0.312) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.680 (+/-0.364) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.705 (+/-0.340) for {'C': 22908676.527677726, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.658 (+/-0.310) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.681 (+/-0.367) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.656 (+/-0.312) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.656 (+/-0.310) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.679 (+/-0.371) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.731 (+/-0.311) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.704 (+/-0.343) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.680 (+/-0.364) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.653 (+/-0.312) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.680 (+/-0.364) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.705 (+/-0.341) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.700 (+/-0.309) for {'C': 0.1, 'kernel': 'linear'}
0.698 (+/-0.306) for {'C': 1.0, 'kernel': 'linear'}
0.694 (+/-0.301) for {'C': 10.0, 'kernel': 'linear'}
0.657 (+/-0.313) for {'C': 100.0, 'kernel': 'linear'}
0.659 (+/-0.313) for {'C': 1000.0, 'kernel': 'linear'}
0.659 (+/-0.313) for {'C': 10000.0, 'kernel': 'linear'}
0.634 (+/-0.325) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.05 0.17 0.07 6
1 0.99 0.97 0.98 623
avg / total 0.98 0.96 0.97 629
本轮grid search结果,得到最好的参数选择是: {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.7310881977079726
第0轮loop中,tunemodel函数得到的最优参数是: ([{'C': array([13182567.38556406, 13427649.61137862, 13677288.25595846,
13931568.02945303, 14190575.21689092, 14454397.70745927,
14723125.02432717, 14996848.35502371, 15275660.58238074,
15559656.31605075, 15848931.92461113]), 'kernel': ['rbf'], 'gamma': array([5.75439937e-06, 5.86138165e-06, 5.97035287e-06, 6.08135001e-06,
6.19441075e-06, 6.30957344e-06, 6.42687717e-06, 6.54636174e-06,
6.66806769e-06, 6.79203633e-06, 6.91830971e-06])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}], {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}, 0.7310881977079726)
这是第0次迭代微调C和gamma。
第0次迭代,得到delta: [1394534.21715187 0. ]
执行tunemodel()函数前,使用的grid search parameter是:
[{'C': array([13182567.38556406, 13427649.61137862, 13677288.25595846,
13931568.02945303, 14190575.21689092, 14454397.70745927,
14723125.02432717, 14996848.35502371, 15275660.58238074,
15559656.31605075, 15848931.92461113]), 'kernel': ['rbf'], 'gamma': array([5.75439937e-06, 5.86138165e-06, 5.97035287e-06, 6.08135001e-06,
6.19441075e-06, 6.30957344e-06, 6.42687717e-06, 6.54636174e-06,
6.66806769e-06, 6.79203633e-06, 6.91830971e-06])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.603 (+/-0.201) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.585 (+/-0.170) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 5.861381645140292e-06}
0.581 (+/-0.164) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 5.970352865838371e-06}
0.588 (+/-0.186) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 6.081350012787179e-06}
0.597 (+/-0.191) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 6.194410750767812e-06}
0.599 (+/-0.193) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.592 (+/-0.177) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 6.426877173170205e-06}
0.598 (+/-0.197) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 6.546361740672756e-06}
0.600 (+/-0.197) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 6.668067692136225e-06}
0.587 (+/-0.191) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 6.792036326171847e-06}
0.588 (+/-0.191) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.603 (+/-0.201) for {'C': 13427649.611378616, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.587 (+/-0.170) for {'C': 13427649.611378616, 'kernel': 'rbf', 'gamma': 5.861381645140292e-06}
0.581 (+/-0.164) for {'C': 13427649.611378616, 'kernel': 'rbf', 'gamma': 5.970352865838371e-06}
0.588 (+/-0.186) for {'C': 13427649.611378616, 'kernel': 'rbf', 'gamma': 6.081350012787179e-06}
0.597 (+/-0.191) for {'C': 13427649.611378616, 'kernel': 'rbf', 'gamma': 6.194410750767812e-06}
0.599 (+/-0.193) for {'C': 13427649.611378616, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.592 (+/-0.177) for {'C': 13427649.611378616, 'kernel': 'rbf', 'gamma': 6.426877173170205e-06}
0.598 (+/-0.197) for {'C': 13427649.611378616, 'kernel': 'rbf', 'gamma': 6.546361740672756e-06}
0.600 (+/-0.197) for {'C': 13427649.611378616, 'kernel': 'rbf', 'gamma': 6.668067692136225e-06}
0.587 (+/-0.191) for {'C': 13427649.611378616, 'kernel': 'rbf', 'gamma': 6.792036326171847e-06}
0.588 (+/-0.191) for {'C': 13427649.611378616, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.603 (+/-0.201) for {'C': 13677288.255958464, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.587 (+/-0.170) for {'C': 13677288.255958464, 'kernel': 'rbf', 'gamma': 5.861381645140292e-06}
0.581 (+/-0.164) for {'C': 13677288.255958464, 'kernel': 'rbf', 'gamma': 5.970352865838371e-06}
0.588 (+/-0.186) for {'C': 13677288.255958464, 'kernel': 'rbf', 'gamma': 6.081350012787179e-06}
0.597 (+/-0.191) for {'C': 13677288.255958464, 'kernel': 'rbf', 'gamma': 6.194410750767812e-06}
0.599 (+/-0.193) for {'C': 13677288.255958464, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.592 (+/-0.177) for {'C': 13677288.255958464, 'kernel': 'rbf', 'gamma': 6.426877173170205e-06}
0.598 (+/-0.197) for {'C': 13677288.255958464, 'kernel': 'rbf', 'gamma': 6.546361740672756e-06}
0.600 (+/-0.197) for {'C': 13677288.255958464, 'kernel': 'rbf', 'gamma': 6.668067692136225e-06}
0.587 (+/-0.191) for {'C': 13677288.255958464, 'kernel': 'rbf', 'gamma': 6.792036326171847e-06}
0.588 (+/-0.191) for {'C': 13677288.255958464, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.603 (+/-0.201) for {'C': 13931568.029453032, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.585 (+/-0.170) for {'C': 13931568.029453032, 'kernel': 'rbf', 'gamma': 5.861381645140292e-06}
0.581 (+/-0.164) for {'C': 13931568.029453032, 'kernel': 'rbf', 'gamma': 5.970352865838371e-06}
0.588 (+/-0.186) for {'C': 13931568.029453032, 'kernel': 'rbf', 'gamma': 6.081350012787179e-06}
0.597 (+/-0.191) for {'C': 13931568.029453032, 'kernel': 'rbf', 'gamma': 6.194410750767812e-06}
0.599 (+/-0.193) for {'C': 13931568.029453032, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.592 (+/-0.177) for {'C': 13931568.029453032, 'kernel': 'rbf', 'gamma': 6.426877173170205e-06}
0.598 (+/-0.197) for {'C': 13931568.029453032, 'kernel': 'rbf', 'gamma': 6.546361740672756e-06}
0.596 (+/-0.193) for {'C': 13931568.029453032, 'kernel': 'rbf', 'gamma': 6.668067692136225e-06}
0.587 (+/-0.191) for {'C': 13931568.029453032, 'kernel': 'rbf', 'gamma': 6.792036326171847e-06}
0.588 (+/-0.191) for {'C': 13931568.029453032, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.603 (+/-0.201) for {'C': 14190575.216890918, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.587 (+/-0.170) for {'C': 14190575.216890918, 'kernel': 'rbf', 'gamma': 5.861381645140292e-06}
0.581 (+/-0.164) for {'C': 14190575.216890918, 'kernel': 'rbf', 'gamma': 5.970352865838371e-06}
0.588 (+/-0.186) for {'C': 14190575.216890918, 'kernel': 'rbf', 'gamma': 6.081350012787179e-06}
0.597 (+/-0.191) for {'C': 14190575.216890918, 'kernel': 'rbf', 'gamma': 6.194410750767812e-06}
0.599 (+/-0.193) for {'C': 14190575.216890918, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.591 (+/-0.177) for {'C': 14190575.216890918, 'kernel': 'rbf', 'gamma': 6.426877173170205e-06}
0.598 (+/-0.197) for {'C': 14190575.216890918, 'kernel': 'rbf', 'gamma': 6.546361740672756e-06}
0.596 (+/-0.193) for {'C': 14190575.216890918, 'kernel': 'rbf', 'gamma': 6.668067692136225e-06}
0.587 (+/-0.191) for {'C': 14190575.216890918, 'kernel': 'rbf', 'gamma': 6.792036326171847e-06}
0.588 (+/-0.191) for {'C': 14190575.216890918, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.603 (+/-0.201) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.584 (+/-0.169) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 5.861381645140292e-06}
0.581 (+/-0.164) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 5.970352865838371e-06}
0.586 (+/-0.185) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 6.081350012787179e-06}
0.597 (+/-0.191) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 6.194410750767812e-06}
0.599 (+/-0.193) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.591 (+/-0.177) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 6.426877173170205e-06}
0.598 (+/-0.197) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 6.546361740672756e-06}
0.596 (+/-0.193) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 6.668067692136225e-06}
0.587 (+/-0.191) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 6.792036326171847e-06}
0.588 (+/-0.191) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.603 (+/-0.201) for {'C': 14723125.024327174, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.584 (+/-0.169) for {'C': 14723125.024327174, 'kernel': 'rbf', 'gamma': 5.861381645140292e-06}
0.581 (+/-0.164) for {'C': 14723125.024327174, 'kernel': 'rbf', 'gamma': 5.970352865838371e-06}
0.588 (+/-0.186) for {'C': 14723125.024327174, 'kernel': 'rbf', 'gamma': 6.081350012787179e-06}
0.597 (+/-0.191) for {'C': 14723125.024327174, 'kernel': 'rbf', 'gamma': 6.194410750767812e-06}
0.599 (+/-0.193) for {'C': 14723125.024327174, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.591 (+/-0.177) for {'C': 14723125.024327174, 'kernel': 'rbf', 'gamma': 6.426877173170205e-06}
0.598 (+/-0.197) for {'C': 14723125.024327174, 'kernel': 'rbf', 'gamma': 6.546361740672756e-06}
0.596 (+/-0.193) for {'C': 14723125.024327174, 'kernel': 'rbf', 'gamma': 6.668067692136225e-06}
0.587 (+/-0.191) for {'C': 14723125.024327174, 'kernel': 'rbf', 'gamma': 6.792036326171847e-06}
0.588 (+/-0.191) for {'C': 14723125.024327174, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.603 (+/-0.201) for {'C': 14996848.355023714, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.584 (+/-0.169) for {'C': 14996848.355023714, 'kernel': 'rbf', 'gamma': 5.861381645140292e-06}
0.581 (+/-0.164) for {'C': 14996848.355023714, 'kernel': 'rbf', 'gamma': 5.970352865838371e-06}
0.588 (+/-0.186) for {'C': 14996848.355023714, 'kernel': 'rbf', 'gamma': 6.081350012787179e-06}
0.597 (+/-0.191) for {'C': 14996848.355023714, 'kernel': 'rbf', 'gamma': 6.194410750767812e-06}
0.599 (+/-0.193) for {'C': 14996848.355023714, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.591 (+/-0.177) for {'C': 14996848.355023714, 'kernel': 'rbf', 'gamma': 6.426877173170205e-06}
0.598 (+/-0.197) for {'C': 14996848.355023714, 'kernel': 'rbf', 'gamma': 6.546361740672756e-06}
0.596 (+/-0.193) for {'C': 14996848.355023714, 'kernel': 'rbf', 'gamma': 6.668067692136225e-06}
0.587 (+/-0.191) for {'C': 14996848.355023714, 'kernel': 'rbf', 'gamma': 6.792036326171847e-06}
0.588 (+/-0.191) for {'C': 14996848.355023714, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.603 (+/-0.201) for {'C': 15275660.582380736, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.584 (+/-0.169) for {'C': 15275660.582380736, 'kernel': 'rbf', 'gamma': 5.861381645140292e-06}
0.581 (+/-0.164) for {'C': 15275660.582380736, 'kernel': 'rbf', 'gamma': 5.970352865838371e-06}
0.588 (+/-0.186) for {'C': 15275660.582380736, 'kernel': 'rbf', 'gamma': 6.081350012787179e-06}
0.597 (+/-0.191) for {'C': 15275660.582380736, 'kernel': 'rbf', 'gamma': 6.194410750767812e-06}
0.599 (+/-0.193) for {'C': 15275660.582380736, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.591 (+/-0.177) for {'C': 15275660.582380736, 'kernel': 'rbf', 'gamma': 6.426877173170205e-06}
0.598 (+/-0.197) for {'C': 15275660.582380736, 'kernel': 'rbf', 'gamma': 6.546361740672756e-06}
0.596 (+/-0.193) for {'C': 15275660.582380736, 'kernel': 'rbf', 'gamma': 6.668067692136225e-06}
0.587 (+/-0.191) for {'C': 15275660.582380736, 'kernel': 'rbf', 'gamma': 6.792036326171847e-06}
0.588 (+/-0.191) for {'C': 15275660.582380736, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.603 (+/-0.201) for {'C': 15559656.31605075, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.587 (+/-0.170) for {'C': 15559656.31605075, 'kernel': 'rbf', 'gamma': 5.861381645140292e-06}
0.581 (+/-0.164) for {'C': 15559656.31605075, 'kernel': 'rbf', 'gamma': 5.970352865838371e-06}
0.588 (+/-0.186) for {'C': 15559656.31605075, 'kernel': 'rbf', 'gamma': 6.081350012787179e-06}
0.597 (+/-0.191) for {'C': 15559656.31605075, 'kernel': 'rbf', 'gamma': 6.194410750767812e-06}
0.599 (+/-0.193) for {'C': 15559656.31605075, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.591 (+/-0.177) for {'C': 15559656.31605075, 'kernel': 'rbf', 'gamma': 6.426877173170205e-06}
0.598 (+/-0.197) for {'C': 15559656.31605075, 'kernel': 'rbf', 'gamma': 6.546361740672756e-06}
0.596 (+/-0.193) for {'C': 15559656.31605075, 'kernel': 'rbf', 'gamma': 6.668067692136225e-06}
0.587 (+/-0.191) for {'C': 15559656.31605075, 'kernel': 'rbf', 'gamma': 6.792036326171847e-06}
0.588 (+/-0.191) for {'C': 15559656.31605075, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.603 (+/-0.201) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.587 (+/-0.170) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 5.861381645140292e-06}
0.581 (+/-0.164) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 5.970352865838371e-06}
0.588 (+/-0.186) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.081350012787179e-06}
0.597 (+/-0.191) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.194410750767812e-06}
0.598 (+/-0.194) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.591 (+/-0.177) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.426877173170205e-06}
0.598 (+/-0.197) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.546361740672756e-06}
0.596 (+/-0.193) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.668067692136225e-06}
0.587 (+/-0.191) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.792036326171847e-06}
0.588 (+/-0.191) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.685 (+/-0.297) for {'C': 1.0, 'kernel': 'linear'}
0.607 (+/-0.182) for {'C': 10.0, 'kernel': 'linear'}
0.608 (+/-0.197) for {'C': 100.0, 'kernel': 'linear'}
0.589 (+/-0.189) for {'C': 1000.0, 'kernel': 'linear'}
0.585 (+/-0.189) for {'C': 10000.0, 'kernel': 'linear'}
0.580 (+/-0.195) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.663 (+/-0.315) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.662 (+/-0.314) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 5.861381645140292e-06}
0.661 (+/-0.309) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 5.970352865838371e-06}
0.661 (+/-0.310) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 6.081350012787179e-06}
0.662 (+/-0.314) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 6.194410750767812e-06}
0.662 (+/-0.315) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.662 (+/-0.313) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 6.426877173170205e-06}
0.663 (+/-0.315) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 6.546361740672756e-06}
0.662 (+/-0.314) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 6.668067692136225e-06}
0.661 (+/-0.311) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 6.792036326171847e-06}
0.661 (+/-0.309) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.663 (+/-0.315) for {'C': 13427649.611378616, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.662 (+/-0.315) for {'C': 13427649.611378616, 'kernel': 'rbf', 'gamma': 5.861381645140292e-06}
0.661 (+/-0.309) for {'C': 13427649.611378616, 'kernel': 'rbf', 'gamma': 5.970352865838371e-06}
0.661 (+/-0.310) for {'C': 13427649.611378616, 'kernel': 'rbf', 'gamma': 6.081350012787179e-06}
0.662 (+/-0.314) for {'C': 13427649.611378616, 'kernel': 'rbf', 'gamma': 6.194410750767812e-06}
0.662 (+/-0.314) for {'C': 13427649.611378616, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.662 (+/-0.313) for {'C': 13427649.611378616, 'kernel': 'rbf', 'gamma': 6.426877173170205e-06}
0.663 (+/-0.315) for {'C': 13427649.611378616, 'kernel': 'rbf', 'gamma': 6.546361740672756e-06}
0.662 (+/-0.314) for {'C': 13427649.611378616, 'kernel': 'rbf', 'gamma': 6.668067692136225e-06}
0.661 (+/-0.311) for {'C': 13427649.611378616, 'kernel': 'rbf', 'gamma': 6.792036326171847e-06}
0.661 (+/-0.309) for {'C': 13427649.611378616, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.663 (+/-0.315) for {'C': 13677288.255958464, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.662 (+/-0.315) for {'C': 13677288.255958464, 'kernel': 'rbf', 'gamma': 5.861381645140292e-06}
0.661 (+/-0.309) for {'C': 13677288.255958464, 'kernel': 'rbf', 'gamma': 5.970352865838371e-06}
0.661 (+/-0.310) for {'C': 13677288.255958464, 'kernel': 'rbf', 'gamma': 6.081350012787179e-06}
0.662 (+/-0.314) for {'C': 13677288.255958464, 'kernel': 'rbf', 'gamma': 6.194410750767812e-06}
0.662 (+/-0.314) for {'C': 13677288.255958464, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.662 (+/-0.314) for {'C': 13677288.255958464, 'kernel': 'rbf', 'gamma': 6.426877173170205e-06}
0.663 (+/-0.315) for {'C': 13677288.255958464, 'kernel': 'rbf', 'gamma': 6.546361740672756e-06}
0.662 (+/-0.314) for {'C': 13677288.255958464, 'kernel': 'rbf', 'gamma': 6.668067692136225e-06}
0.661 (+/-0.311) for {'C': 13677288.255958464, 'kernel': 'rbf', 'gamma': 6.792036326171847e-06}
0.661 (+/-0.309) for {'C': 13677288.255958464, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.663 (+/-0.315) for {'C': 13931568.029453032, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.662 (+/-0.314) for {'C': 13931568.029453032, 'kernel': 'rbf', 'gamma': 5.861381645140292e-06}
0.661 (+/-0.309) for {'C': 13931568.029453032, 'kernel': 'rbf', 'gamma': 5.970352865838371e-06}
0.661 (+/-0.310) for {'C': 13931568.029453032, 'kernel': 'rbf', 'gamma': 6.081350012787179e-06}
0.662 (+/-0.314) for {'C': 13931568.029453032, 'kernel': 'rbf', 'gamma': 6.194410750767812e-06}
0.662 (+/-0.315) for {'C': 13931568.029453032, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.662 (+/-0.313) for {'C': 13931568.029453032, 'kernel': 'rbf', 'gamma': 6.426877173170205e-06}
0.663 (+/-0.315) for {'C': 13931568.029453032, 'kernel': 'rbf', 'gamma': 6.546361740672756e-06}
0.662 (+/-0.314) for {'C': 13931568.029453032, 'kernel': 'rbf', 'gamma': 6.668067692136225e-06}
0.661 (+/-0.311) for {'C': 13931568.029453032, 'kernel': 'rbf', 'gamma': 6.792036326171847e-06}
0.661 (+/-0.309) for {'C': 13931568.029453032, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.663 (+/-0.315) for {'C': 14190575.216890918, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.662 (+/-0.315) for {'C': 14190575.216890918, 'kernel': 'rbf', 'gamma': 5.861381645140292e-06}
0.661 (+/-0.309) for {'C': 14190575.216890918, 'kernel': 'rbf', 'gamma': 5.970352865838371e-06}
0.661 (+/-0.310) for {'C': 14190575.216890918, 'kernel': 'rbf', 'gamma': 6.081350012787179e-06}
0.662 (+/-0.314) for {'C': 14190575.216890918, 'kernel': 'rbf', 'gamma': 6.194410750767812e-06}
0.662 (+/-0.314) for {'C': 14190575.216890918, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.662 (+/-0.313) for {'C': 14190575.216890918, 'kernel': 'rbf', 'gamma': 6.426877173170205e-06}
0.663 (+/-0.315) for {'C': 14190575.216890918, 'kernel': 'rbf', 'gamma': 6.546361740672756e-06}
0.662 (+/-0.314) for {'C': 14190575.216890918, 'kernel': 'rbf', 'gamma': 6.668067692136225e-06}
0.661 (+/-0.311) for {'C': 14190575.216890918, 'kernel': 'rbf', 'gamma': 6.792036326171847e-06}
0.661 (+/-0.309) for {'C': 14190575.216890918, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.663 (+/-0.315) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.662 (+/-0.314) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 5.861381645140292e-06}
0.661 (+/-0.309) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 5.970352865838371e-06}
0.661 (+/-0.310) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 6.081350012787179e-06}
0.662 (+/-0.314) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 6.194410750767812e-06}
0.662 (+/-0.314) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.662 (+/-0.313) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 6.426877173170205e-06}
0.663 (+/-0.315) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 6.546361740672756e-06}
0.662 (+/-0.314) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 6.668067692136225e-06}
0.661 (+/-0.311) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 6.792036326171847e-06}
0.661 (+/-0.309) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.663 (+/-0.315) for {'C': 14723125.024327174, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.662 (+/-0.314) for {'C': 14723125.024327174, 'kernel': 'rbf', 'gamma': 5.861381645140292e-06}
0.660 (+/-0.309) for {'C': 14723125.024327174, 'kernel': 'rbf', 'gamma': 5.970352865838371e-06}
0.661 (+/-0.310) for {'C': 14723125.024327174, 'kernel': 'rbf', 'gamma': 6.081350012787179e-06}
0.662 (+/-0.314) for {'C': 14723125.024327174, 'kernel': 'rbf', 'gamma': 6.194410750767812e-06}
0.662 (+/-0.315) for {'C': 14723125.024327174, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.662 (+/-0.313) for {'C': 14723125.024327174, 'kernel': 'rbf', 'gamma': 6.426877173170205e-06}
0.663 (+/-0.315) for {'C': 14723125.024327174, 'kernel': 'rbf', 'gamma': 6.546361740672756e-06}
0.662 (+/-0.314) for {'C': 14723125.024327174, 'kernel': 'rbf', 'gamma': 6.668067692136225e-06}
0.660 (+/-0.311) for {'C': 14723125.024327174, 'kernel': 'rbf', 'gamma': 6.792036326171847e-06}
0.661 (+/-0.309) for {'C': 14723125.024327174, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.663 (+/-0.315) for {'C': 14996848.355023714, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.662 (+/-0.314) for {'C': 14996848.355023714, 'kernel': 'rbf', 'gamma': 5.861381645140292e-06}
0.660 (+/-0.309) for {'C': 14996848.355023714, 'kernel': 'rbf', 'gamma': 5.970352865838371e-06}
0.661 (+/-0.310) for {'C': 14996848.355023714, 'kernel': 'rbf', 'gamma': 6.081350012787179e-06}
0.662 (+/-0.314) for {'C': 14996848.355023714, 'kernel': 'rbf', 'gamma': 6.194410750767812e-06}
0.662 (+/-0.314) for {'C': 14996848.355023714, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.662 (+/-0.313) for {'C': 14996848.355023714, 'kernel': 'rbf', 'gamma': 6.426877173170205e-06}
0.663 (+/-0.315) for {'C': 14996848.355023714, 'kernel': 'rbf', 'gamma': 6.546361740672756e-06}
0.662 (+/-0.314) for {'C': 14996848.355023714, 'kernel': 'rbf', 'gamma': 6.668067692136225e-06}
0.660 (+/-0.311) for {'C': 14996848.355023714, 'kernel': 'rbf', 'gamma': 6.792036326171847e-06}
0.661 (+/-0.309) for {'C': 14996848.355023714, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.662 (+/-0.316) for {'C': 15275660.582380736, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.662 (+/-0.314) for {'C': 15275660.582380736, 'kernel': 'rbf', 'gamma': 5.861381645140292e-06}
0.660 (+/-0.309) for {'C': 15275660.582380736, 'kernel': 'rbf', 'gamma': 5.970352865838371e-06}
0.661 (+/-0.310) for {'C': 15275660.582380736, 'kernel': 'rbf', 'gamma': 6.081350012787179e-06}
0.662 (+/-0.314) for {'C': 15275660.582380736, 'kernel': 'rbf', 'gamma': 6.194410750767812e-06}
0.662 (+/-0.314) for {'C': 15275660.582380736, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.662 (+/-0.313) for {'C': 15275660.582380736, 'kernel': 'rbf', 'gamma': 6.426877173170205e-06}
0.663 (+/-0.315) for {'C': 15275660.582380736, 'kernel': 'rbf', 'gamma': 6.546361740672756e-06}
0.662 (+/-0.314) for {'C': 15275660.582380736, 'kernel': 'rbf', 'gamma': 6.668067692136225e-06}
0.660 (+/-0.312) for {'C': 15275660.582380736, 'kernel': 'rbf', 'gamma': 6.792036326171847e-06}
0.661 (+/-0.309) for {'C': 15275660.582380736, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.662 (+/-0.316) for {'C': 15559656.31605075, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.662 (+/-0.315) for {'C': 15559656.31605075, 'kernel': 'rbf', 'gamma': 5.861381645140292e-06}
0.660 (+/-0.309) for {'C': 15559656.31605075, 'kernel': 'rbf', 'gamma': 5.970352865838371e-06}
0.661 (+/-0.310) for {'C': 15559656.31605075, 'kernel': 'rbf', 'gamma': 6.081350012787179e-06}
0.662 (+/-0.314) for {'C': 15559656.31605075, 'kernel': 'rbf', 'gamma': 6.194410750767812e-06}
0.662 (+/-0.315) for {'C': 15559656.31605075, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.662 (+/-0.313) for {'C': 15559656.31605075, 'kernel': 'rbf', 'gamma': 6.426877173170205e-06}
0.662 (+/-0.315) for {'C': 15559656.31605075, 'kernel': 'rbf', 'gamma': 6.546361740672756e-06}
0.662 (+/-0.314) for {'C': 15559656.31605075, 'kernel': 'rbf', 'gamma': 6.668067692136225e-06}
0.660 (+/-0.312) for {'C': 15559656.31605075, 'kernel': 'rbf', 'gamma': 6.792036326171847e-06}
0.661 (+/-0.309) for {'C': 15559656.31605075, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.662 (+/-0.316) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.662 (+/-0.315) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 5.861381645140292e-06}
0.660 (+/-0.309) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 5.970352865838371e-06}
0.661 (+/-0.310) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.081350012787179e-06}
0.662 (+/-0.314) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.194410750767812e-06}
0.662 (+/-0.315) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.662 (+/-0.313) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.426877173170205e-06}
0.662 (+/-0.315) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.546361740672756e-06}
0.662 (+/-0.314) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.668067692136225e-06}
0.660 (+/-0.312) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.792036326171847e-06}
0.661 (+/-0.309) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.698 (+/-0.306) for {'C': 1.0, 'kernel': 'linear'}
0.696 (+/-0.303) for {'C': 10.0, 'kernel': 'linear'}
0.663 (+/-0.314) for {'C': 100.0, 'kernel': 'linear'}
0.660 (+/-0.314) for {'C': 1000.0, 'kernel': 'linear'}
0.659 (+/-0.312) for {'C': 10000.0, 'kernel': 'linear'}
0.634 (+/-0.325) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.20 0.17 0.18 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.99 0.99 629
本轮grid search结果,得到最好的参数选择是: {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6983958900156649
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.585 (+/-0.192) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.568 (+/-0.155) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 5.861381645140292e-06}
0.572 (+/-0.161) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 5.970352865838371e-06}
0.579 (+/-0.183) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 6.081350012787179e-06}
0.590 (+/-0.190) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 6.194410750767812e-06}
0.593 (+/-0.187) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.587 (+/-0.172) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 6.426877173170205e-06}
0.582 (+/-0.187) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 6.546361740672756e-06}
0.588 (+/-0.194) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 6.668067692136225e-06}
0.587 (+/-0.187) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 6.792036326171847e-06}
0.589 (+/-0.187) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.585 (+/-0.192) for {'C': 13427649.611378616, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.570 (+/-0.156) for {'C': 13427649.611378616, 'kernel': 'rbf', 'gamma': 5.861381645140292e-06}
0.572 (+/-0.161) for {'C': 13427649.611378616, 'kernel': 'rbf', 'gamma': 5.970352865838371e-06}
0.579 (+/-0.183) for {'C': 13427649.611378616, 'kernel': 'rbf', 'gamma': 6.081350012787179e-06}
0.590 (+/-0.190) for {'C': 13427649.611378616, 'kernel': 'rbf', 'gamma': 6.194410750767812e-06}
0.593 (+/-0.187) for {'C': 13427649.611378616, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.587 (+/-0.172) for {'C': 13427649.611378616, 'kernel': 'rbf', 'gamma': 6.426877173170205e-06}
0.582 (+/-0.187) for {'C': 13427649.611378616, 'kernel': 'rbf', 'gamma': 6.546361740672756e-06}
0.588 (+/-0.194) for {'C': 13427649.611378616, 'kernel': 'rbf', 'gamma': 6.668067692136225e-06}
0.587 (+/-0.187) for {'C': 13427649.611378616, 'kernel': 'rbf', 'gamma': 6.792036326171847e-06}
0.589 (+/-0.187) for {'C': 13427649.611378616, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.585 (+/-0.192) for {'C': 13677288.255958464, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.569 (+/-0.156) for {'C': 13677288.255958464, 'kernel': 'rbf', 'gamma': 5.861381645140292e-06}
0.572 (+/-0.161) for {'C': 13677288.255958464, 'kernel': 'rbf', 'gamma': 5.970352865838371e-06}
0.579 (+/-0.183) for {'C': 13677288.255958464, 'kernel': 'rbf', 'gamma': 6.081350012787179e-06}
0.590 (+/-0.190) for {'C': 13677288.255958464, 'kernel': 'rbf', 'gamma': 6.194410750767812e-06}
0.593 (+/-0.187) for {'C': 13677288.255958464, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.587 (+/-0.172) for {'C': 13677288.255958464, 'kernel': 'rbf', 'gamma': 6.426877173170205e-06}
0.582 (+/-0.187) for {'C': 13677288.255958464, 'kernel': 'rbf', 'gamma': 6.546361740672756e-06}
0.588 (+/-0.194) for {'C': 13677288.255958464, 'kernel': 'rbf', 'gamma': 6.668067692136225e-06}
0.587 (+/-0.187) for {'C': 13677288.255958464, 'kernel': 'rbf', 'gamma': 6.792036326171847e-06}
0.589 (+/-0.187) for {'C': 13677288.255958464, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.585 (+/-0.192) for {'C': 13931568.029453032, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.567 (+/-0.155) for {'C': 13931568.029453032, 'kernel': 'rbf', 'gamma': 5.861381645140292e-06}
0.572 (+/-0.161) for {'C': 13931568.029453032, 'kernel': 'rbf', 'gamma': 5.970352865838371e-06}
0.579 (+/-0.183) for {'C': 13931568.029453032, 'kernel': 'rbf', 'gamma': 6.081350012787179e-06}
0.590 (+/-0.190) for {'C': 13931568.029453032, 'kernel': 'rbf', 'gamma': 6.194410750767812e-06}
0.593 (+/-0.187) for {'C': 13931568.029453032, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.587 (+/-0.172) for {'C': 13931568.029453032, 'kernel': 'rbf', 'gamma': 6.426877173170205e-06}
0.582 (+/-0.187) for {'C': 13931568.029453032, 'kernel': 'rbf', 'gamma': 6.546361740672756e-06}
0.584 (+/-0.189) for {'C': 13931568.029453032, 'kernel': 'rbf', 'gamma': 6.668067692136225e-06}
0.587 (+/-0.187) for {'C': 13931568.029453032, 'kernel': 'rbf', 'gamma': 6.792036326171847e-06}
0.589 (+/-0.187) for {'C': 13931568.029453032, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.585 (+/-0.192) for {'C': 14190575.216890918, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.569 (+/-0.156) for {'C': 14190575.216890918, 'kernel': 'rbf', 'gamma': 5.861381645140292e-06}
0.572 (+/-0.161) for {'C': 14190575.216890918, 'kernel': 'rbf', 'gamma': 5.970352865838371e-06}
0.579 (+/-0.183) for {'C': 14190575.216890918, 'kernel': 'rbf', 'gamma': 6.081350012787179e-06}
0.590 (+/-0.190) for {'C': 14190575.216890918, 'kernel': 'rbf', 'gamma': 6.194410750767812e-06}
0.593 (+/-0.187) for {'C': 14190575.216890918, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.587 (+/-0.172) for {'C': 14190575.216890918, 'kernel': 'rbf', 'gamma': 6.426877173170205e-06}
0.582 (+/-0.187) for {'C': 14190575.216890918, 'kernel': 'rbf', 'gamma': 6.546361740672756e-06}
0.584 (+/-0.189) for {'C': 14190575.216890918, 'kernel': 'rbf', 'gamma': 6.668067692136225e-06}
0.587 (+/-0.187) for {'C': 14190575.216890918, 'kernel': 'rbf', 'gamma': 6.792036326171847e-06}
0.589 (+/-0.187) for {'C': 14190575.216890918, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.585 (+/-0.192) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.569 (+/-0.156) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 5.861381645140292e-06}
0.572 (+/-0.161) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 5.970352865838371e-06}
0.578 (+/-0.183) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 6.081350012787179e-06}
0.590 (+/-0.190) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 6.194410750767812e-06}
0.599 (+/-0.178) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.587 (+/-0.172) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 6.426877173170205e-06}
0.582 (+/-0.187) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 6.546361740672756e-06}
0.584 (+/-0.188) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 6.668067692136225e-06}
0.587 (+/-0.187) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 6.792036326171847e-06}
0.589 (+/-0.187) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.585 (+/-0.192) for {'C': 14723125.024327174, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.569 (+/-0.156) for {'C': 14723125.024327174, 'kernel': 'rbf', 'gamma': 5.861381645140292e-06}
0.572 (+/-0.161) for {'C': 14723125.024327174, 'kernel': 'rbf', 'gamma': 5.970352865838371e-06}
0.579 (+/-0.183) for {'C': 14723125.024327174, 'kernel': 'rbf', 'gamma': 6.081350012787179e-06}
0.590 (+/-0.190) for {'C': 14723125.024327174, 'kernel': 'rbf', 'gamma': 6.194410750767812e-06}
0.599 (+/-0.178) for {'C': 14723125.024327174, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.587 (+/-0.172) for {'C': 14723125.024327174, 'kernel': 'rbf', 'gamma': 6.426877173170205e-06}
0.582 (+/-0.187) for {'C': 14723125.024327174, 'kernel': 'rbf', 'gamma': 6.546361740672756e-06}
0.584 (+/-0.188) for {'C': 14723125.024327174, 'kernel': 'rbf', 'gamma': 6.668067692136225e-06}
0.587 (+/-0.187) for {'C': 14723125.024327174, 'kernel': 'rbf', 'gamma': 6.792036326171847e-06}
0.589 (+/-0.187) for {'C': 14723125.024327174, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.585 (+/-0.192) for {'C': 14996848.355023714, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.569 (+/-0.156) for {'C': 14996848.355023714, 'kernel': 'rbf', 'gamma': 5.861381645140292e-06}
0.572 (+/-0.161) for {'C': 14996848.355023714, 'kernel': 'rbf', 'gamma': 5.970352865838371e-06}
0.579 (+/-0.183) for {'C': 14996848.355023714, 'kernel': 'rbf', 'gamma': 6.081350012787179e-06}
0.589 (+/-0.190) for {'C': 14996848.355023714, 'kernel': 'rbf', 'gamma': 6.194410750767812e-06}
0.599 (+/-0.178) for {'C': 14996848.355023714, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.587 (+/-0.172) for {'C': 14996848.355023714, 'kernel': 'rbf', 'gamma': 6.426877173170205e-06}
0.582 (+/-0.187) for {'C': 14996848.355023714, 'kernel': 'rbf', 'gamma': 6.546361740672756e-06}
0.584 (+/-0.188) for {'C': 14996848.355023714, 'kernel': 'rbf', 'gamma': 6.668067692136225e-06}
0.587 (+/-0.187) for {'C': 14996848.355023714, 'kernel': 'rbf', 'gamma': 6.792036326171847e-06}
0.589 (+/-0.187) for {'C': 14996848.355023714, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.585 (+/-0.192) for {'C': 15275660.582380736, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.569 (+/-0.156) for {'C': 15275660.582380736, 'kernel': 'rbf', 'gamma': 5.861381645140292e-06}
0.572 (+/-0.161) for {'C': 15275660.582380736, 'kernel': 'rbf', 'gamma': 5.970352865838371e-06}
0.579 (+/-0.183) for {'C': 15275660.582380736, 'kernel': 'rbf', 'gamma': 6.081350012787179e-06}
0.589 (+/-0.190) for {'C': 15275660.582380736, 'kernel': 'rbf', 'gamma': 6.194410750767812e-06}
0.599 (+/-0.178) for {'C': 15275660.582380736, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.587 (+/-0.172) for {'C': 15275660.582380736, 'kernel': 'rbf', 'gamma': 6.426877173170205e-06}
0.582 (+/-0.187) for {'C': 15275660.582380736, 'kernel': 'rbf', 'gamma': 6.546361740672756e-06}
0.584 (+/-0.188) for {'C': 15275660.582380736, 'kernel': 'rbf', 'gamma': 6.668067692136225e-06}
0.587 (+/-0.187) for {'C': 15275660.582380736, 'kernel': 'rbf', 'gamma': 6.792036326171847e-06}
0.589 (+/-0.187) for {'C': 15275660.582380736, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.585 (+/-0.192) for {'C': 15559656.31605075, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.569 (+/-0.156) for {'C': 15559656.31605075, 'kernel': 'rbf', 'gamma': 5.861381645140292e-06}
0.572 (+/-0.161) for {'C': 15559656.31605075, 'kernel': 'rbf', 'gamma': 5.970352865838371e-06}
0.579 (+/-0.183) for {'C': 15559656.31605075, 'kernel': 'rbf', 'gamma': 6.081350012787179e-06}
0.589 (+/-0.190) for {'C': 15559656.31605075, 'kernel': 'rbf', 'gamma': 6.194410750767812e-06}
0.599 (+/-0.178) for {'C': 15559656.31605075, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.587 (+/-0.172) for {'C': 15559656.31605075, 'kernel': 'rbf', 'gamma': 6.426877173170205e-06}
0.582 (+/-0.187) for {'C': 15559656.31605075, 'kernel': 'rbf', 'gamma': 6.546361740672756e-06}
0.584 (+/-0.188) for {'C': 15559656.31605075, 'kernel': 'rbf', 'gamma': 6.668067692136225e-06}
0.587 (+/-0.187) for {'C': 15559656.31605075, 'kernel': 'rbf', 'gamma': 6.792036326171847e-06}
0.589 (+/-0.187) for {'C': 15559656.31605075, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.585 (+/-0.192) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.569 (+/-0.156) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 5.861381645140292e-06}
0.572 (+/-0.161) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 5.970352865838371e-06}
0.579 (+/-0.183) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.081350012787179e-06}
0.589 (+/-0.190) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.194410750767812e-06}
0.599 (+/-0.178) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.587 (+/-0.172) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.426877173170205e-06}
0.582 (+/-0.187) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.546361740672756e-06}
0.584 (+/-0.188) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.668067692136225e-06}
0.587 (+/-0.187) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.792036326171847e-06}
0.589 (+/-0.187) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.798 (+/-0.437) for {'C': 0.1, 'kernel': 'linear'}
0.660 (+/-0.219) for {'C': 1.0, 'kernel': 'linear'}
0.584 (+/-0.147) for {'C': 10.0, 'kernel': 'linear'}
0.587 (+/-0.195) for {'C': 100.0, 'kernel': 'linear'}
0.585 (+/-0.188) for {'C': 1000.0, 'kernel': 'linear'}
0.585 (+/-0.189) for {'C': 10000.0, 'kernel': 'linear'}
0.580 (+/-0.195) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.680 (+/-0.370) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.702 (+/-0.334) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 5.861381645140292e-06}
0.679 (+/-0.287) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 5.970352865838371e-06}
0.680 (+/-0.289) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 6.081350012787179e-06}
0.682 (+/-0.290) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 6.194410750767812e-06}
0.706 (+/-0.342) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.705 (+/-0.341) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 6.426877173170205e-06}
0.680 (+/-0.369) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 6.546361740672756e-06}
0.681 (+/-0.364) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 6.668067692136225e-06}
0.706 (+/-0.338) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 6.792036326171847e-06}
0.705 (+/-0.343) for {'C': 13182567.385564055, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.679 (+/-0.370) for {'C': 13427649.611378616, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.703 (+/-0.336) for {'C': 13427649.611378616, 'kernel': 'rbf', 'gamma': 5.861381645140292e-06}
0.679 (+/-0.287) for {'C': 13427649.611378616, 'kernel': 'rbf', 'gamma': 5.970352865838371e-06}
0.680 (+/-0.289) for {'C': 13427649.611378616, 'kernel': 'rbf', 'gamma': 6.081350012787179e-06}
0.682 (+/-0.290) for {'C': 13427649.611378616, 'kernel': 'rbf', 'gamma': 6.194410750767812e-06}
0.706 (+/-0.342) for {'C': 13427649.611378616, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.705 (+/-0.342) for {'C': 13427649.611378616, 'kernel': 'rbf', 'gamma': 6.426877173170205e-06}
0.680 (+/-0.369) for {'C': 13427649.611378616, 'kernel': 'rbf', 'gamma': 6.546361740672756e-06}
0.682 (+/-0.365) for {'C': 13427649.611378616, 'kernel': 'rbf', 'gamma': 6.668067692136225e-06}
0.706 (+/-0.338) for {'C': 13427649.611378616, 'kernel': 'rbf', 'gamma': 6.792036326171847e-06}
0.705 (+/-0.343) for {'C': 13427649.611378616, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.679 (+/-0.370) for {'C': 13677288.255958464, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.702 (+/-0.334) for {'C': 13677288.255958464, 'kernel': 'rbf', 'gamma': 5.861381645140292e-06}
0.679 (+/-0.287) for {'C': 13677288.255958464, 'kernel': 'rbf', 'gamma': 5.970352865838371e-06}
0.680 (+/-0.289) for {'C': 13677288.255958464, 'kernel': 'rbf', 'gamma': 6.081350012787179e-06}
0.682 (+/-0.290) for {'C': 13677288.255958464, 'kernel': 'rbf', 'gamma': 6.194410750767812e-06}
0.706 (+/-0.342) for {'C': 13677288.255958464, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.705 (+/-0.341) for {'C': 13677288.255958464, 'kernel': 'rbf', 'gamma': 6.426877173170205e-06}
0.680 (+/-0.369) for {'C': 13677288.255958464, 'kernel': 'rbf', 'gamma': 6.546361740672756e-06}
0.682 (+/-0.365) for {'C': 13677288.255958464, 'kernel': 'rbf', 'gamma': 6.668067692136225e-06}
0.706 (+/-0.338) for {'C': 13677288.255958464, 'kernel': 'rbf', 'gamma': 6.792036326171847e-06}
0.705 (+/-0.343) for {'C': 13677288.255958464, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.679 (+/-0.371) for {'C': 13931568.029453032, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.702 (+/-0.333) for {'C': 13931568.029453032, 'kernel': 'rbf', 'gamma': 5.861381645140292e-06}
0.679 (+/-0.287) for {'C': 13931568.029453032, 'kernel': 'rbf', 'gamma': 5.970352865838371e-06}
0.680 (+/-0.289) for {'C': 13931568.029453032, 'kernel': 'rbf', 'gamma': 6.081350012787179e-06}
0.682 (+/-0.290) for {'C': 13931568.029453032, 'kernel': 'rbf', 'gamma': 6.194410750767812e-06}
0.706 (+/-0.342) for {'C': 13931568.029453032, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.705 (+/-0.342) for {'C': 13931568.029453032, 'kernel': 'rbf', 'gamma': 6.426877173170205e-06}
0.680 (+/-0.369) for {'C': 13931568.029453032, 'kernel': 'rbf', 'gamma': 6.546361740672756e-06}
0.681 (+/-0.364) for {'C': 13931568.029453032, 'kernel': 'rbf', 'gamma': 6.668067692136225e-06}
0.706 (+/-0.338) for {'C': 13931568.029453032, 'kernel': 'rbf', 'gamma': 6.792036326171847e-06}
0.705 (+/-0.343) for {'C': 13931568.029453032, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.679 (+/-0.371) for {'C': 14190575.216890918, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.702 (+/-0.334) for {'C': 14190575.216890918, 'kernel': 'rbf', 'gamma': 5.861381645140292e-06}
0.679 (+/-0.287) for {'C': 14190575.216890918, 'kernel': 'rbf', 'gamma': 5.970352865838371e-06}
0.680 (+/-0.289) for {'C': 14190575.216890918, 'kernel': 'rbf', 'gamma': 6.081350012787179e-06}
0.682 (+/-0.290) for {'C': 14190575.216890918, 'kernel': 'rbf', 'gamma': 6.194410750767812e-06}
0.706 (+/-0.342) for {'C': 14190575.216890918, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.705 (+/-0.342) for {'C': 14190575.216890918, 'kernel': 'rbf', 'gamma': 6.426877173170205e-06}
0.680 (+/-0.369) for {'C': 14190575.216890918, 'kernel': 'rbf', 'gamma': 6.546361740672756e-06}
0.681 (+/-0.364) for {'C': 14190575.216890918, 'kernel': 'rbf', 'gamma': 6.668067692136225e-06}
0.706 (+/-0.338) for {'C': 14190575.216890918, 'kernel': 'rbf', 'gamma': 6.792036326171847e-06}
0.705 (+/-0.343) for {'C': 14190575.216890918, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.679 (+/-0.371) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.702 (+/-0.334) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 5.861381645140292e-06}
0.679 (+/-0.287) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 5.970352865838371e-06}
0.680 (+/-0.289) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 6.081350012787179e-06}
0.682 (+/-0.290) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 6.194410750767812e-06}
0.731 (+/-0.310) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.705 (+/-0.342) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 6.426877173170205e-06}
0.680 (+/-0.369) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 6.546361740672756e-06}
0.681 (+/-0.365) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 6.668067692136225e-06}
0.706 (+/-0.338) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 6.792036326171847e-06}
0.705 (+/-0.343) for {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.679 (+/-0.371) for {'C': 14723125.024327174, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.702 (+/-0.334) for {'C': 14723125.024327174, 'kernel': 'rbf', 'gamma': 5.861381645140292e-06}
0.679 (+/-0.287) for {'C': 14723125.024327174, 'kernel': 'rbf', 'gamma': 5.970352865838371e-06}
0.680 (+/-0.289) for {'C': 14723125.024327174, 'kernel': 'rbf', 'gamma': 6.081350012787179e-06}
0.682 (+/-0.290) for {'C': 14723125.024327174, 'kernel': 'rbf', 'gamma': 6.194410750767812e-06}
0.731 (+/-0.310) for {'C': 14723125.024327174, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.705 (+/-0.342) for {'C': 14723125.024327174, 'kernel': 'rbf', 'gamma': 6.426877173170205e-06}
0.680 (+/-0.369) for {'C': 14723125.024327174, 'kernel': 'rbf', 'gamma': 6.546361740672756e-06}
0.681 (+/-0.365) for {'C': 14723125.024327174, 'kernel': 'rbf', 'gamma': 6.668067692136225e-06}
0.706 (+/-0.338) for {'C': 14723125.024327174, 'kernel': 'rbf', 'gamma': 6.792036326171847e-06}
0.705 (+/-0.343) for {'C': 14723125.024327174, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.679 (+/-0.371) for {'C': 14996848.355023714, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.702 (+/-0.334) for {'C': 14996848.355023714, 'kernel': 'rbf', 'gamma': 5.861381645140292e-06}
0.679 (+/-0.287) for {'C': 14996848.355023714, 'kernel': 'rbf', 'gamma': 5.970352865838371e-06}
0.680 (+/-0.289) for {'C': 14996848.355023714, 'kernel': 'rbf', 'gamma': 6.081350012787179e-06}
0.682 (+/-0.290) for {'C': 14996848.355023714, 'kernel': 'rbf', 'gamma': 6.194410750767812e-06}
0.731 (+/-0.310) for {'C': 14996848.355023714, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.705 (+/-0.342) for {'C': 14996848.355023714, 'kernel': 'rbf', 'gamma': 6.426877173170205e-06}
0.680 (+/-0.368) for {'C': 14996848.355023714, 'kernel': 'rbf', 'gamma': 6.546361740672756e-06}
0.681 (+/-0.365) for {'C': 14996848.355023714, 'kernel': 'rbf', 'gamma': 6.668067692136225e-06}
0.706 (+/-0.338) for {'C': 14996848.355023714, 'kernel': 'rbf', 'gamma': 6.792036326171847e-06}
0.705 (+/-0.343) for {'C': 14996848.355023714, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.679 (+/-0.371) for {'C': 15275660.582380736, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.702 (+/-0.334) for {'C': 15275660.582380736, 'kernel': 'rbf', 'gamma': 5.861381645140292e-06}
0.679 (+/-0.287) for {'C': 15275660.582380736, 'kernel': 'rbf', 'gamma': 5.970352865838371e-06}
0.680 (+/-0.290) for {'C': 15275660.582380736, 'kernel': 'rbf', 'gamma': 6.081350012787179e-06}
0.682 (+/-0.290) for {'C': 15275660.582380736, 'kernel': 'rbf', 'gamma': 6.194410750767812e-06}
0.731 (+/-0.310) for {'C': 15275660.582380736, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.705 (+/-0.342) for {'C': 15275660.582380736, 'kernel': 'rbf', 'gamma': 6.426877173170205e-06}
0.680 (+/-0.369) for {'C': 15275660.582380736, 'kernel': 'rbf', 'gamma': 6.546361740672756e-06}
0.681 (+/-0.365) for {'C': 15275660.582380736, 'kernel': 'rbf', 'gamma': 6.668067692136225e-06}
0.705 (+/-0.339) for {'C': 15275660.582380736, 'kernel': 'rbf', 'gamma': 6.792036326171847e-06}
0.705 (+/-0.343) for {'C': 15275660.582380736, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.679 (+/-0.371) for {'C': 15559656.31605075, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.702 (+/-0.334) for {'C': 15559656.31605075, 'kernel': 'rbf', 'gamma': 5.861381645140292e-06}
0.679 (+/-0.288) for {'C': 15559656.31605075, 'kernel': 'rbf', 'gamma': 5.970352865838371e-06}
0.680 (+/-0.290) for {'C': 15559656.31605075, 'kernel': 'rbf', 'gamma': 6.081350012787179e-06}
0.682 (+/-0.290) for {'C': 15559656.31605075, 'kernel': 'rbf', 'gamma': 6.194410750767812e-06}
0.731 (+/-0.311) for {'C': 15559656.31605075, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.705 (+/-0.342) for {'C': 15559656.31605075, 'kernel': 'rbf', 'gamma': 6.426877173170205e-06}
0.680 (+/-0.370) for {'C': 15559656.31605075, 'kernel': 'rbf', 'gamma': 6.546361740672756e-06}
0.681 (+/-0.365) for {'C': 15559656.31605075, 'kernel': 'rbf', 'gamma': 6.668067692136225e-06}
0.705 (+/-0.339) for {'C': 15559656.31605075, 'kernel': 'rbf', 'gamma': 6.792036326171847e-06}
0.705 (+/-0.343) for {'C': 15559656.31605075, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.679 (+/-0.370) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.702 (+/-0.334) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 5.861381645140292e-06}
0.679 (+/-0.288) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 5.970352865838371e-06}
0.680 (+/-0.290) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.081350012787179e-06}
0.682 (+/-0.290) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.194410750767812e-06}
0.731 (+/-0.311) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.705 (+/-0.342) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.426877173170205e-06}
0.680 (+/-0.370) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.546361740672756e-06}
0.681 (+/-0.365) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.668067692136225e-06}
0.705 (+/-0.339) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.792036326171847e-06}
0.705 (+/-0.343) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.700 (+/-0.309) for {'C': 0.1, 'kernel': 'linear'}
0.698 (+/-0.306) for {'C': 1.0, 'kernel': 'linear'}
0.694 (+/-0.301) for {'C': 10.0, 'kernel': 'linear'}
0.657 (+/-0.313) for {'C': 100.0, 'kernel': 'linear'}
0.659 (+/-0.313) for {'C': 1000.0, 'kernel': 'linear'}
0.659 (+/-0.313) for {'C': 10000.0, 'kernel': 'linear'}
0.634 (+/-0.325) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.05 0.17 0.07 6
1 0.99 0.97 0.98 623
avg / total 0.98 0.96 0.97 629
本轮grid search结果,得到最好的参数选择是: {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.7310881977079726
第1轮loop中,tunemodel函数得到的最优参数是: ([{'C': array([14190575.21689092, 14242951.64973292, 14295521.40037034,
14348285.18232589, 14401243.71175568, 14454397.70745927,
14507747.89088912, 14561294.98616055, 14615039.72006164,
14668982.82206284, 14723125.02432717]), 'kernel': ['rbf'], 'gamma': array([6.19441075e-06, 6.21727389e-06, 6.24022142e-06, 6.26325365e-06,
6.28637088e-06, 6.30957344e-06, 6.33286164e-06, 6.35623580e-06,
6.37969623e-06, 6.40324324e-06, 6.42687717e-06])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}], {'C': 14454397.707459265, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}, 0.7310881977079726)
这是第1次迭代微调C和gamma。
第1次迭代,得到delta: [0. 0.]
SVC模型clf_op_iter的参数是: {'kernel': 'rbf', 'decision_function_shape': 'ovr', 'class_weight': {-1: 15}, 'tol': 0.001, 'gamma': 6.309573444801928e-06, 'random_state': 0, 'cache_size': 200, 'verbose': False, 'degree': 3, 'C': 14454397.707459265, 'probability': False, 'shrinking': True, 'coef0': 0.0, 'max_iter': -1}
训练模型SVC对训练样本的预测准确率: 0.9539333805811481
测试集中,预测为舞弊样本的有: (array([ 0, 1, 2, ..., 1254, 1255, 1256], dtype=int64),)
测试集中,实际为舞弊样本的有: (array([1246, 1247, 1248, 1249, 1250, 1251, 1252, 1253, 1254, 1255, 1256],
dtype=int64),)
预测的舞弊样本数目: 1150
训练模型SVC对测试样本的预测准确率: 0.18143160878809356
以上是第41次特征筛选。
第41次特征筛选,AUC值是: 0.4970815701152779
X_train_iter_svc.shape is: (1257, 11)
X_test_iter_svc.shape is: (1257, 11)
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.722 (+/-0.473) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.604 (+/-0.307) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.660 (+/-0.219) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.612 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.698 (+/-0.306) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.20 0.17 0.18 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.99 0.99 629
本轮grid search结果,得到最好的参数选择是: {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6982356336054085
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.660 (+/-0.219) for {'C': 1.0, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.581 (+/-0.185) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.698 (+/-0.306) for {'C': 1.0, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.659 (+/-0.314) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6982356336054085
RBF的粗grid search最佳超参: {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001} RBF的粗grid search最高分值: 0.6982356336054085
linear的粗grid search最佳超参: {'C': 1.0, 'kernel': 'linear'} linear的粗grid search最高分值: 0.6982356336054085
粗grid search得到的parameter是:
[{'C': array([1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02, 1.e+03, 1.e+04, 1.e+05,
1.e+06, 1.e+07, 1.e+08]), 'kernel': ['rbf'], 'gamma': array([1.e-08, 1.e-07, 1.e-06, 1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01,
1.e+00, 1.e+01, 1.e+02])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.848 (+/-0.459) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.848 (+/-0.459) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.685 (+/-0.297) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.611 (+/-0.308) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.848 (+/-0.459) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.660 (+/-0.219) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.637 (+/-0.286) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.604 (+/-0.307) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.848 (+/-0.459) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.660 (+/-0.219) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.606 (+/-0.183) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.590 (+/-0.312) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.604 (+/-0.307) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.848 (+/-0.459) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.660 (+/-0.219) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.601 (+/-0.179) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.590 (+/-0.187) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.599 (+/-0.306) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.604 (+/-0.307) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.848 (+/-0.459) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.660 (+/-0.219) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.604 (+/-0.182) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.587 (+/-0.185) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.567 (+/-0.197) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.599 (+/-0.306) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.604 (+/-0.307) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.748 (+/-0.396) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.660 (+/-0.219) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.602 (+/-0.182) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.597 (+/-0.211) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.582 (+/-0.186) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.571 (+/-0.192) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.599 (+/-0.306) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.604 (+/-0.307) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.848 (+/-0.460) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.703 (+/-0.418) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.628 (+/-0.288) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.586 (+/-0.187) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.578 (+/-0.186) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.573 (+/-0.192) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.572 (+/-0.192) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.599 (+/-0.306) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.604 (+/-0.307) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.793 (+/-0.440) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.690 (+/-0.435) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.622 (+/-0.290) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.570 (+/-0.154) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.578 (+/-0.186) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.574 (+/-0.191) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.572 (+/-0.192) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.599 (+/-0.306) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.604 (+/-0.307) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.685 (+/-0.297) for {'C': 1.0, 'kernel': 'linear'}
0.604 (+/-0.181) for {'C': 10.0, 'kernel': 'linear'}
0.609 (+/-0.201) for {'C': 100.0, 'kernel': 'linear'}
0.584 (+/-0.186) for {'C': 1000.0, 'kernel': 'linear'}
0.577 (+/-0.192) for {'C': 10000.0, 'kernel': 'linear'}
0.576 (+/-0.191) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [1]
检查交叉验证集中测试集标签是否有预测不到的值: {-1}
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 1.00 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.658 (+/-0.217) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.658 (+/-0.217) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.698 (+/-0.306) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.236) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.007) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.658 (+/-0.217) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.306) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.696 (+/-0.303) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.611 (+/-0.240) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.497 (+/-0.006) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.658 (+/-0.217) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.698 (+/-0.306) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.696 (+/-0.303) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.587 (+/-0.230) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.612 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.497 (+/-0.006) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.658 (+/-0.217) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.698 (+/-0.306) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.695 (+/-0.303) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.662 (+/-0.315) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.611 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.497 (+/-0.006) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.658 (+/-0.217) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.698 (+/-0.306) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.696 (+/-0.304) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.661 (+/-0.313) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.585 (+/-0.234) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.497 (+/-0.006) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.699 (+/-0.308) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.698 (+/-0.306) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.695 (+/-0.300) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.661 (+/-0.318) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.659 (+/-0.313) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.610 (+/-0.238) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.238) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.237) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.497 (+/-0.006) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.683 (+/-0.296) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.683 (+/-0.258) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.696 (+/-0.303) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.660 (+/-0.310) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.658 (+/-0.311) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.632 (+/-0.323) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.611 (+/-0.237) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.238) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.237) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.497 (+/-0.006) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.699 (+/-0.307) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.633 (+/-0.237) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.695 (+/-0.302) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.659 (+/-0.310) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.657 (+/-0.309) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.633 (+/-0.322) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.610 (+/-0.238) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.238) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.237) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.497 (+/-0.006) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.698 (+/-0.306) for {'C': 1.0, 'kernel': 'linear'}
0.696 (+/-0.302) for {'C': 10.0, 'kernel': 'linear'}
0.663 (+/-0.315) for {'C': 100.0, 'kernel': 'linear'}
0.660 (+/-0.314) for {'C': 1000.0, 'kernel': 'linear'}
0.635 (+/-0.324) for {'C': 10000.0, 'kernel': 'linear'}
0.634 (+/-0.325) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6991982026547944
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.669 (+/-0.296) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.667 (+/-0.292) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.656 (+/-0.384) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.642 (+/-0.204) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.199) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.591 (+/-0.295) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.642 (+/-0.204) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.624 (+/-0.186) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.280) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.604 (+/-0.307) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.642 (+/-0.204) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.624 (+/-0.186) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.582 (+/-0.153) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.594 (+/-0.308) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.604 (+/-0.307) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.642 (+/-0.204) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.624 (+/-0.186) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.578 (+/-0.146) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.578 (+/-0.187) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.599 (+/-0.306) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.604 (+/-0.307) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.747 (+/-0.502) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.642 (+/-0.204) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.624 (+/-0.186) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.578 (+/-0.146) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.575 (+/-0.186) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.573 (+/-0.192) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.599 (+/-0.306) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.604 (+/-0.307) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.647 (+/-0.460) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.656 (+/-0.231) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.622 (+/-0.197) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.584 (+/-0.154) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.575 (+/-0.187) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.580 (+/-0.186) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.571 (+/-0.192) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.599 (+/-0.306) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.604 (+/-0.307) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.848 (+/-0.460) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.542 (+/-0.143) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.593 (+/-0.280) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.583 (+/-0.189) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.577 (+/-0.186) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.573 (+/-0.192) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.572 (+/-0.192) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.599 (+/-0.306) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.604 (+/-0.307) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.793 (+/-0.440) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.553 (+/-0.296) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.609 (+/-0.286) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.568 (+/-0.155) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.578 (+/-0.186) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.574 (+/-0.191) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.572 (+/-0.192) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.599 (+/-0.306) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.604 (+/-0.307) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.798 (+/-0.437) for {'C': 0.1, 'kernel': 'linear'}
0.660 (+/-0.219) for {'C': 1.0, 'kernel': 'linear'}
0.585 (+/-0.147) for {'C': 10.0, 'kernel': 'linear'}
0.582 (+/-0.190) for {'C': 100.0, 'kernel': 'linear'}
0.581 (+/-0.185) for {'C': 1000.0, 'kernel': 'linear'}
0.576 (+/-0.191) for {'C': 10000.0, 'kernel': 'linear'}
0.575 (+/-0.191) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.682 (+/-0.294) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.698 (+/-0.306) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.494 (+/-0.012) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.306) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.305) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.611 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.007) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.698 (+/-0.306) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.304) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.693 (+/-0.304) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.611 (+/-0.241) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.497 (+/-0.006) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.698 (+/-0.306) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.304) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.693 (+/-0.304) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.608 (+/-0.241) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.612 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.497 (+/-0.006) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.698 (+/-0.306) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.304) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.692 (+/-0.303) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.657 (+/-0.312) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.611 (+/-0.239) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.497 (+/-0.006) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.617 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.698 (+/-0.306) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.697 (+/-0.304) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.692 (+/-0.304) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.656 (+/-0.312) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.608 (+/-0.242) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.497 (+/-0.006) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.575 (+/-0.229) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.698 (+/-0.308) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.697 (+/-0.303) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.693 (+/-0.303) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.656 (+/-0.311) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.658 (+/-0.312) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.610 (+/-0.238) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.238) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.237) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.497 (+/-0.006) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.700 (+/-0.309) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.655 (+/-0.246) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.692 (+/-0.299) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.680 (+/-0.364) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.657 (+/-0.310) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.632 (+/-0.323) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.611 (+/-0.237) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.238) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.237) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.497 (+/-0.006) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.699 (+/-0.307) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.604 (+/-0.160) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.694 (+/-0.304) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.679 (+/-0.363) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.657 (+/-0.309) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.633 (+/-0.322) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.610 (+/-0.238) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.238) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.237) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.497 (+/-0.006) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.700 (+/-0.309) for {'C': 0.1, 'kernel': 'linear'}
0.698 (+/-0.306) for {'C': 1.0, 'kernel': 'linear'}
0.694 (+/-0.301) for {'C': 10.0, 'kernel': 'linear'}
0.657 (+/-0.313) for {'C': 100.0, 'kernel': 'linear'}
0.659 (+/-0.314) for {'C': 1000.0, 'kernel': 'linear'}
0.634 (+/-0.324) for {'C': 10000.0, 'kernel': 'linear'}
0.633 (+/-0.324) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.7
发现最优参数gamma为原先的最大/最小值,直接重新设置超参。
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-09}
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-08}
0.748 (+/-0.396) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-07}
0.660 (+/-0.219) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-06}
0.602 (+/-0.182) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-05}
0.597 (+/-0.211) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 0.0001}
0.582 (+/-0.186) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-09}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-08}
0.723 (+/-0.359) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-07}
0.672 (+/-0.295) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-06}
0.598 (+/-0.164) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-05}
0.586 (+/-0.188) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 0.0001}
0.580 (+/-0.186) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-09}
0.747 (+/-0.502) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-08}
0.681 (+/-0.297) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-07}
0.653 (+/-0.286) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-06}
0.611 (+/-0.183) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-05}
0.579 (+/-0.187) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 0.0001}
0.579 (+/-0.186) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-09}
0.747 (+/-0.502) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-08}
0.698 (+/-0.353) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-07}
0.649 (+/-0.286) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-06}
0.597 (+/-0.192) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-05}
0.579 (+/-0.186) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 0.0001}
0.575 (+/-0.191) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-09}
0.797 (+/-0.492) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-08}
0.710 (+/-0.406) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-07}
0.645 (+/-0.287) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-06}
0.587 (+/-0.187) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-05}
0.577 (+/-0.185) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 0.0001}
0.574 (+/-0.192) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-09}
0.848 (+/-0.460) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-08}
0.703 (+/-0.418) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-07}
0.628 (+/-0.288) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-06}
0.586 (+/-0.187) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-05}
0.578 (+/-0.186) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 0.0001}
0.573 (+/-0.192) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-09}
0.823 (+/-0.451) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-08}
0.703 (+/-0.418) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-07}
0.624 (+/-0.290) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-06}
0.586 (+/-0.187) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-05}
0.577 (+/-0.186) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 0.0001}
0.573 (+/-0.192) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-10}
0.747 (+/-0.502) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-09}
0.806 (+/-0.436) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-08}
0.690 (+/-0.372) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-07}
0.624 (+/-0.290) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-06}
0.584 (+/-0.186) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-05}
0.577 (+/-0.186) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.0001}
0.573 (+/-0.192) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-10}
0.747 (+/-0.502) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-09}
0.798 (+/-0.437) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-08}
0.715 (+/-0.415) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-07}
0.624 (+/-0.290) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-06}
0.570 (+/-0.154) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-05}
0.577 (+/-0.186) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 0.0001}
0.573 (+/-0.192) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-10}
0.797 (+/-0.492) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-09}
0.793 (+/-0.440) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-08}
0.740 (+/-0.449) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-07}
0.622 (+/-0.290) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-06}
0.570 (+/-0.154) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-05}
0.578 (+/-0.186) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 0.0001}
0.574 (+/-0.191) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-10}
0.848 (+/-0.460) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-09}
0.793 (+/-0.440) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-08}
0.690 (+/-0.435) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-07}
0.622 (+/-0.290) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-06}
0.570 (+/-0.154) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-05}
0.578 (+/-0.186) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 0.0001}
0.574 (+/-0.191) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.685 (+/-0.297) for {'C': 1.0, 'kernel': 'linear'}
0.604 (+/-0.181) for {'C': 10.0, 'kernel': 'linear'}
0.609 (+/-0.201) for {'C': 100.0, 'kernel': 'linear'}
0.584 (+/-0.186) for {'C': 1000.0, 'kernel': 'linear'}
0.577 (+/-0.192) for {'C': 10000.0, 'kernel': 'linear'}
0.576 (+/-0.191) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [1]
检查交叉验证集中测试集标签是否有预测不到的值: {-1}
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 1.00 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-09}
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-08}
0.699 (+/-0.308) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-07}
0.698 (+/-0.306) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-06}
0.695 (+/-0.300) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-05}
0.661 (+/-0.318) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 0.0001}
0.659 (+/-0.313) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-09}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-08}
0.699 (+/-0.308) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-07}
0.698 (+/-0.306) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-06}
0.695 (+/-0.300) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-05}
0.660 (+/-0.316) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 0.0001}
0.658 (+/-0.313) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-09}
0.617 (+/-0.238) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-08}
0.698 (+/-0.307) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-07}
0.697 (+/-0.304) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-06}
0.679 (+/-0.288) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-05}
0.659 (+/-0.314) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 0.0001}
0.658 (+/-0.313) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-09}
0.617 (+/-0.238) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-08}
0.699 (+/-0.308) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-07}
0.697 (+/-0.304) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-06}
0.662 (+/-0.310) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-05}
0.659 (+/-0.313) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 0.0001}
0.633 (+/-0.324) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-09}
0.642 (+/-0.236) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-08}
0.697 (+/-0.303) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-07}
0.697 (+/-0.304) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-06}
0.660 (+/-0.309) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-05}
0.658 (+/-0.311) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 0.0001}
0.632 (+/-0.324) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-09}
0.683 (+/-0.296) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-08}
0.683 (+/-0.258) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-07}
0.696 (+/-0.303) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-06}
0.660 (+/-0.310) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-05}
0.658 (+/-0.311) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 0.0001}
0.632 (+/-0.323) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-09}
0.683 (+/-0.296) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-08}
0.660 (+/-0.221) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-07}
0.695 (+/-0.302) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-06}
0.660 (+/-0.310) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-05}
0.657 (+/-0.310) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 0.0001}
0.633 (+/-0.322) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-10}
0.617 (+/-0.238) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-09}
0.700 (+/-0.308) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-08}
0.656 (+/-0.219) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-07}
0.695 (+/-0.303) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-06}
0.660 (+/-0.310) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-05}
0.657 (+/-0.309) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.0001}
0.632 (+/-0.322) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-10}
0.617 (+/-0.238) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-09}
0.699 (+/-0.307) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-08}
0.653 (+/-0.218) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-07}
0.695 (+/-0.303) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-06}
0.659 (+/-0.309) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-05}
0.657 (+/-0.309) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 0.0001}
0.633 (+/-0.322) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-10}
0.642 (+/-0.236) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-09}
0.699 (+/-0.307) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-08}
0.651 (+/-0.219) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-07}
0.695 (+/-0.302) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-06}
0.659 (+/-0.310) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-05}
0.657 (+/-0.308) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 0.0001}
0.633 (+/-0.322) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-10}
0.683 (+/-0.296) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-09}
0.699 (+/-0.307) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-08}
0.633 (+/-0.237) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-07}
0.695 (+/-0.302) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-06}
0.659 (+/-0.310) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-05}
0.657 (+/-0.309) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 0.0001}
0.633 (+/-0.322) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.698 (+/-0.306) for {'C': 1.0, 'kernel': 'linear'}
0.696 (+/-0.302) for {'C': 10.0, 'kernel': 'linear'}
0.663 (+/-0.315) for {'C': 100.0, 'kernel': 'linear'}
0.660 (+/-0.314) for {'C': 1000.0, 'kernel': 'linear'}
0.635 (+/-0.324) for {'C': 10000.0, 'kernel': 'linear'}
0.634 (+/-0.325) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-08}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6995187154753071
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-09}
0.647 (+/-0.460) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-08}
0.656 (+/-0.231) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-07}
0.622 (+/-0.197) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-06}
0.584 (+/-0.154) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-05}
0.575 (+/-0.187) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 0.0001}
0.580 (+/-0.186) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-09}
0.747 (+/-0.502) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-08}
0.640 (+/-0.209) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-07}
0.614 (+/-0.292) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-06}
0.590 (+/-0.158) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-05}
0.575 (+/-0.187) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 0.0001}
0.579 (+/-0.186) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-09}
0.747 (+/-0.502) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-08}
0.626 (+/-0.188) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-07}
0.593 (+/-0.280) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-06}
0.600 (+/-0.182) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-05}
0.574 (+/-0.186) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 0.0001}
0.579 (+/-0.186) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-09}
0.848 (+/-0.459) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-08}
0.600 (+/-0.216) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-07}
0.591 (+/-0.283) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-06}
0.588 (+/-0.194) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-05}
0.576 (+/-0.186) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 0.0001}
0.575 (+/-0.191) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-09}
0.848 (+/-0.460) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-08}
0.555 (+/-0.143) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-07}
0.592 (+/-0.280) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-06}
0.584 (+/-0.189) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-05}
0.576 (+/-0.186) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 0.0001}
0.574 (+/-0.192) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-10}
0.647 (+/-0.460) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-09}
0.848 (+/-0.460) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-08}
0.543 (+/-0.144) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-07}
0.593 (+/-0.280) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-06}
0.583 (+/-0.189) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-05}
0.577 (+/-0.186) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 0.0001}
0.573 (+/-0.192) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-10}
0.747 (+/-0.502) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-09}
0.806 (+/-0.436) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-08}
0.536 (+/-0.144) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-07}
0.608 (+/-0.287) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-06}
0.583 (+/-0.189) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-05}
0.577 (+/-0.186) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 0.0001}
0.573 (+/-0.192) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-10}
0.747 (+/-0.502) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-09}
0.798 (+/-0.437) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-08}
0.529 (+/-0.146) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-07}
0.609 (+/-0.286) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-06}
0.580 (+/-0.188) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-05}
0.577 (+/-0.186) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.0001}
0.573 (+/-0.192) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-10}
0.848 (+/-0.459) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-09}
0.793 (+/-0.440) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-08}
0.554 (+/-0.296) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-07}
0.605 (+/-0.283) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-06}
0.568 (+/-0.155) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-05}
0.577 (+/-0.186) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 0.0001}
0.573 (+/-0.192) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-10}
0.848 (+/-0.460) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-09}
0.793 (+/-0.440) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-08}
0.554 (+/-0.296) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-07}
0.605 (+/-0.283) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-06}
0.568 (+/-0.155) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-05}
0.578 (+/-0.186) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 0.0001}
0.574 (+/-0.191) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-11}
0.647 (+/-0.460) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-10}
0.848 (+/-0.460) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-09}
0.793 (+/-0.440) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-08}
0.553 (+/-0.296) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-07}
0.609 (+/-0.286) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-06}
0.568 (+/-0.155) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-05}
0.578 (+/-0.186) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 0.0001}
0.574 (+/-0.191) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.798 (+/-0.437) for {'C': 0.1, 'kernel': 'linear'}
0.660 (+/-0.219) for {'C': 1.0, 'kernel': 'linear'}
0.585 (+/-0.147) for {'C': 10.0, 'kernel': 'linear'}
0.582 (+/-0.190) for {'C': 100.0, 'kernel': 'linear'}
0.581 (+/-0.185) for {'C': 1000.0, 'kernel': 'linear'}
0.576 (+/-0.191) for {'C': 10000.0, 'kernel': 'linear'}
0.575 (+/-0.191) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-09}
0.575 (+/-0.229) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-08}
0.698 (+/-0.308) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-07}
0.697 (+/-0.303) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-06}
0.693 (+/-0.303) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-05}
0.656 (+/-0.311) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 0.0001}
0.658 (+/-0.312) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-09}
0.617 (+/-0.238) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-08}
0.697 (+/-0.307) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-07}
0.694 (+/-0.297) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-06}
0.716 (+/-0.353) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-05}
0.656 (+/-0.311) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 0.0001}
0.658 (+/-0.313) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-09}
0.617 (+/-0.238) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-08}
0.697 (+/-0.308) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-07}
0.692 (+/-0.297) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-06}
0.699 (+/-0.344) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-05}
0.657 (+/-0.310) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 0.0001}
0.658 (+/-0.313) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-09}
0.658 (+/-0.217) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-08}
0.692 (+/-0.303) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-07}
0.691 (+/-0.296) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-06}
0.681 (+/-0.365) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-05}
0.657 (+/-0.310) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 0.0001}
0.633 (+/-0.324) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-09}
0.683 (+/-0.296) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-08}
0.678 (+/-0.277) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-07}
0.692 (+/-0.298) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-06}
0.681 (+/-0.364) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-05}
0.657 (+/-0.309) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 0.0001}
0.632 (+/-0.324) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-10}
0.575 (+/-0.229) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-09}
0.700 (+/-0.309) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-08}
0.656 (+/-0.248) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-07}
0.692 (+/-0.299) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-06}
0.680 (+/-0.364) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-05}
0.657 (+/-0.310) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 0.0001}
0.632 (+/-0.323) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-10}
0.617 (+/-0.238) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-09}
0.700 (+/-0.308) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-08}
0.652 (+/-0.183) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-07}
0.693 (+/-0.304) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-06}
0.680 (+/-0.364) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-05}
0.657 (+/-0.310) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 0.0001}
0.633 (+/-0.322) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-10}
0.617 (+/-0.238) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-09}
0.699 (+/-0.307) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-08}
0.612 (+/-0.143) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-07}
0.694 (+/-0.304) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-06}
0.679 (+/-0.364) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-05}
0.657 (+/-0.309) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.0001}
0.632 (+/-0.322) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-10}
0.658 (+/-0.217) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-09}
0.699 (+/-0.307) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-08}
0.616 (+/-0.170) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-07}
0.693 (+/-0.303) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-06}
0.679 (+/-0.363) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-05}
0.657 (+/-0.309) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 0.0001}
0.633 (+/-0.322) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-10}
0.683 (+/-0.296) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-09}
0.699 (+/-0.307) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-08}
0.615 (+/-0.177) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-07}
0.693 (+/-0.303) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-06}
0.679 (+/-0.363) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-05}
0.657 (+/-0.308) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 0.0001}
0.633 (+/-0.322) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-11}
0.575 (+/-0.229) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-10}
0.700 (+/-0.309) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-09}
0.699 (+/-0.307) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-08}
0.610 (+/-0.170) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-07}
0.694 (+/-0.304) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-06}
0.679 (+/-0.363) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-05}
0.657 (+/-0.309) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 0.0001}
0.633 (+/-0.322) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.700 (+/-0.309) for {'C': 0.1, 'kernel': 'linear'}
0.698 (+/-0.306) for {'C': 1.0, 'kernel': 'linear'}
0.694 (+/-0.301) for {'C': 10.0, 'kernel': 'linear'}
0.657 (+/-0.313) for {'C': 100.0, 'kernel': 'linear'}
0.659 (+/-0.314) for {'C': 1000.0, 'kernel': 'linear'}
0.634 (+/-0.324) for {'C': 10000.0, 'kernel': 'linear'}
0.633 (+/-0.324) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.04 0.17 0.06 6
1 0.99 0.96 0.98 623
avg / total 0.98 0.95 0.97 629
本轮grid search结果,得到最好的参数选择是: {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-05}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.7161833209662792
循环迭代之前,delta is: [8.41510681e+06 9.99000000e-06]
执行tunemodel()函数前,使用的grid search parameter是:
[{'C': array([1000000. , 1096478.19614319, 1202264.43461741,
1318256.73855641, 1445439.77074593, 1584893.19246111,
1737800.82874938, 1905460.71796325, 2089296.13085404,
2290867.65276777, 2511886.43150958]), 'kernel': ['rbf'], 'gamma': array([1.00000000e-06, 1.58489319e-06, 2.51188643e-06, 3.98107171e-06,
6.30957344e-06, 1.00000000e-05, 1.58489319e-05, 2.51188643e-05,
3.98107171e-05, 6.30957344e-05, 1.00000000e-04])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.660 (+/-0.219) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.693 (+/-0.350) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.635 (+/-0.198) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.620 (+/-0.183) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.593 (+/-0.154) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.602 (+/-0.182) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.594 (+/-0.159) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.608 (+/-0.177) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.595 (+/-0.191) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.591 (+/-0.187) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.597 (+/-0.211) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.660 (+/-0.219) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.668 (+/-0.290) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.635 (+/-0.198) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.610 (+/-0.179) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.590 (+/-0.154) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.602 (+/-0.182) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.594 (+/-0.159) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.610 (+/-0.180) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.598 (+/-0.192) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.590 (+/-0.186) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.591 (+/-0.198) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.660 (+/-0.219) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.643 (+/-0.202) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.633 (+/-0.200) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.606 (+/-0.180) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.596 (+/-0.177) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.594 (+/-0.161) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.598 (+/-0.164) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.598 (+/-0.192) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.595 (+/-0.189) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.588 (+/-0.186) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.588 (+/-0.192) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.676 (+/-0.294) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.668 (+/-0.290) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.633 (+/-0.200) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.604 (+/-0.181) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.596 (+/-0.177) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.593 (+/-0.162) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.599 (+/-0.164) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.609 (+/-0.201) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.591 (+/-0.187) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.590 (+/-0.187) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.586 (+/-0.188) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.672 (+/-0.295) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.668 (+/-0.290) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.653 (+/-0.288) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.594 (+/-0.160) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.596 (+/-0.177) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.598 (+/-0.164) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.608 (+/-0.183) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.609 (+/-0.201) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.588 (+/-0.186) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.590 (+/-0.187) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.586 (+/-0.188) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.672 (+/-0.295) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.643 (+/-0.202) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.622 (+/-0.185) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.593 (+/-0.159) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.597 (+/-0.177) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.598 (+/-0.164) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.604 (+/-0.180) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.610 (+/-0.209) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.588 (+/-0.186) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.588 (+/-0.188) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.586 (+/-0.188) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.664 (+/-0.291) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.643 (+/-0.202) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.621 (+/-0.186) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.594 (+/-0.160) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.597 (+/-0.177) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.600 (+/-0.164) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.602 (+/-0.180) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.608 (+/-0.209) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.587 (+/-0.187) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.584 (+/-0.186) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.583 (+/-0.187) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.664 (+/-0.291) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.664 (+/-0.292) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.618 (+/-0.184) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.595 (+/-0.160) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.596 (+/-0.177) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.600 (+/-0.164) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.590 (+/-0.191) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.606 (+/-0.210) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.587 (+/-0.187) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.583 (+/-0.187) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.581 (+/-0.188) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.653 (+/-0.286) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.664 (+/-0.292) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.641 (+/-0.287) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.595 (+/-0.160) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.600 (+/-0.176) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.608 (+/-0.183) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.590 (+/-0.190) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.603 (+/-0.210) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.586 (+/-0.186) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.583 (+/-0.187) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.581 (+/-0.188) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.655 (+/-0.286) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.664 (+/-0.292) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.637 (+/-0.289) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.591 (+/-0.154) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.600 (+/-0.176) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.608 (+/-0.184) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.592 (+/-0.190) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.608 (+/-0.213) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.587 (+/-0.187) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.583 (+/-0.187) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.579 (+/-0.187) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.653 (+/-0.286) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.664 (+/-0.292) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.606 (+/-0.181) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.589 (+/-0.153) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.593 (+/-0.153) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.611 (+/-0.183) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.594 (+/-0.191) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.601 (+/-0.202) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.584 (+/-0.186) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.583 (+/-0.188) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.579 (+/-0.187) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.685 (+/-0.297) for {'C': 1.0, 'kernel': 'linear'}
0.604 (+/-0.181) for {'C': 10.0, 'kernel': 'linear'}
0.609 (+/-0.201) for {'C': 100.0, 'kernel': 'linear'}
0.584 (+/-0.186) for {'C': 1000.0, 'kernel': 'linear'}
0.577 (+/-0.192) for {'C': 10000.0, 'kernel': 'linear'}
0.576 (+/-0.191) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.698 (+/-0.306) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.698 (+/-0.306) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.698 (+/-0.305) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.697 (+/-0.304) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.695 (+/-0.302) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.695 (+/-0.300) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.695 (+/-0.300) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.679 (+/-0.293) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.662 (+/-0.314) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.661 (+/-0.314) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.661 (+/-0.318) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.698 (+/-0.306) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.698 (+/-0.306) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.698 (+/-0.305) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.696 (+/-0.303) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.695 (+/-0.301) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.695 (+/-0.300) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.695 (+/-0.300) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.679 (+/-0.294) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.663 (+/-0.314) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.661 (+/-0.314) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.660 (+/-0.317) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.698 (+/-0.306) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.698 (+/-0.306) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.697 (+/-0.305) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.696 (+/-0.302) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.695 (+/-0.300) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.695 (+/-0.300) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.695 (+/-0.300) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.663 (+/-0.314) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.662 (+/-0.314) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.661 (+/-0.314) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.660 (+/-0.316) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.698 (+/-0.306) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.698 (+/-0.306) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.697 (+/-0.305) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.695 (+/-0.302) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.694 (+/-0.299) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.694 (+/-0.300) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.695 (+/-0.301) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.663 (+/-0.315) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.662 (+/-0.312) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.661 (+/-0.315) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.660 (+/-0.316) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.698 (+/-0.306) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.698 (+/-0.306) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.697 (+/-0.305) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.695 (+/-0.302) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.694 (+/-0.299) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.695 (+/-0.300) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.695 (+/-0.301) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.663 (+/-0.315) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.662 (+/-0.313) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.661 (+/-0.315) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.660 (+/-0.316) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.698 (+/-0.306) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.698 (+/-0.306) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.697 (+/-0.304) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.695 (+/-0.302) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.695 (+/-0.299) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.695 (+/-0.300) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.679 (+/-0.289) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.663 (+/-0.316) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.661 (+/-0.313) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.661 (+/-0.315) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.660 (+/-0.316) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.698 (+/-0.306) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.698 (+/-0.306) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.697 (+/-0.304) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.695 (+/-0.302) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.694 (+/-0.298) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.695 (+/-0.300) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.678 (+/-0.288) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.663 (+/-0.316) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.661 (+/-0.313) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.660 (+/-0.314) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.660 (+/-0.315) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.698 (+/-0.306) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.698 (+/-0.306) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.697 (+/-0.304) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.695 (+/-0.302) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.694 (+/-0.298) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.695 (+/-0.300) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.662 (+/-0.308) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.663 (+/-0.316) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.661 (+/-0.313) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.660 (+/-0.315) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.659 (+/-0.315) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.697 (+/-0.304) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.698 (+/-0.306) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.696 (+/-0.304) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.695 (+/-0.302) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.695 (+/-0.299) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.695 (+/-0.301) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.662 (+/-0.309) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.662 (+/-0.316) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.661 (+/-0.313) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.660 (+/-0.315) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.659 (+/-0.315) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.698 (+/-0.305) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.698 (+/-0.306) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.696 (+/-0.303) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.695 (+/-0.302) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.695 (+/-0.301) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.695 (+/-0.300) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.662 (+/-0.309) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.662 (+/-0.317) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.661 (+/-0.314) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.659 (+/-0.315) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.658 (+/-0.314) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.697 (+/-0.304) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.698 (+/-0.306) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.695 (+/-0.302) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.695 (+/-0.301) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.695 (+/-0.302) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.679 (+/-0.288) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.662 (+/-0.309) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.662 (+/-0.317) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.660 (+/-0.313) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.659 (+/-0.315) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.659 (+/-0.314) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.698 (+/-0.306) for {'C': 1.0, 'kernel': 'linear'}
0.696 (+/-0.302) for {'C': 10.0, 'kernel': 'linear'}
0.663 (+/-0.315) for {'C': 100.0, 'kernel': 'linear'}
0.660 (+/-0.314) for {'C': 1000.0, 'kernel': 'linear'}
0.635 (+/-0.324) for {'C': 10000.0, 'kernel': 'linear'}
0.634 (+/-0.325) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6983958900156649
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.622 (+/-0.197) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.643 (+/-0.365) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.584 (+/-0.154) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.577 (+/-0.142) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.583 (+/-0.146) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.584 (+/-0.154) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.578 (+/-0.156) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.594 (+/-0.178) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.579 (+/-0.189) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.579 (+/-0.189) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.575 (+/-0.187) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.619 (+/-0.199) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.615 (+/-0.293) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.584 (+/-0.154) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.577 (+/-0.142) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.581 (+/-0.146) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.584 (+/-0.154) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.582 (+/-0.163) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.594 (+/-0.178) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.581 (+/-0.191) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.579 (+/-0.189) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.575 (+/-0.187) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.611 (+/-0.198) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.591 (+/-0.179) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.584 (+/-0.154) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.577 (+/-0.142) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.580 (+/-0.145) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.587 (+/-0.154) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.585 (+/-0.160) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.581 (+/-0.186) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.581 (+/-0.191) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.579 (+/-0.189) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.575 (+/-0.187) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.625 (+/-0.291) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.588 (+/-0.179) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.584 (+/-0.154) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.577 (+/-0.142) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.580 (+/-0.145) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.586 (+/-0.154) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.586 (+/-0.161) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.589 (+/-0.193) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.582 (+/-0.190) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.579 (+/-0.189) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.575 (+/-0.187) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.620 (+/-0.291) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.585 (+/-0.178) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.579 (+/-0.147) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.578 (+/-0.143) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.579 (+/-0.145) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.591 (+/-0.158) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.594 (+/-0.183) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.589 (+/-0.193) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.580 (+/-0.189) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.579 (+/-0.189) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.575 (+/-0.187) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.614 (+/-0.292) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.582 (+/-0.179) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.579 (+/-0.147) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.579 (+/-0.144) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.580 (+/-0.145) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.590 (+/-0.158) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.590 (+/-0.178) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.585 (+/-0.188) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.580 (+/-0.189) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.576 (+/-0.187) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.575 (+/-0.187) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.604 (+/-0.282) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.583 (+/-0.178) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.580 (+/-0.147) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.582 (+/-0.146) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.580 (+/-0.146) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.591 (+/-0.158) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.589 (+/-0.179) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.582 (+/-0.191) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.580 (+/-0.189) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.576 (+/-0.187) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.575 (+/-0.186) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.603 (+/-0.282) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.582 (+/-0.179) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.582 (+/-0.147) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.583 (+/-0.146) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.578 (+/-0.146) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.591 (+/-0.158) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.576 (+/-0.186) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.582 (+/-0.191) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.579 (+/-0.189) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.575 (+/-0.187) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.574 (+/-0.186) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.601 (+/-0.283) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.583 (+/-0.178) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.586 (+/-0.154) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.588 (+/-0.152) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.581 (+/-0.147) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.598 (+/-0.181) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.577 (+/-0.185) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.579 (+/-0.189) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.579 (+/-0.189) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.575 (+/-0.186) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.574 (+/-0.186) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.598 (+/-0.280) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.583 (+/-0.178) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.586 (+/-0.154) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.588 (+/-0.152) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.579 (+/-0.147) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.598 (+/-0.181) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.578 (+/-0.186) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.583 (+/-0.195) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.579 (+/-0.189) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.575 (+/-0.186) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.574 (+/-0.186) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.593 (+/-0.280) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.585 (+/-0.178) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.587 (+/-0.154) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.586 (+/-0.151) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.578 (+/-0.147) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.600 (+/-0.182) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.582 (+/-0.195) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.583 (+/-0.195) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.577 (+/-0.187) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.575 (+/-0.187) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.574 (+/-0.186) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.798 (+/-0.437) for {'C': 0.1, 'kernel': 'linear'}
0.660 (+/-0.219) for {'C': 1.0, 'kernel': 'linear'}
0.585 (+/-0.147) for {'C': 10.0, 'kernel': 'linear'}
0.582 (+/-0.190) for {'C': 100.0, 'kernel': 'linear'}
0.581 (+/-0.185) for {'C': 1000.0, 'kernel': 'linear'}
0.576 (+/-0.191) for {'C': 10000.0, 'kernel': 'linear'}
0.575 (+/-0.191) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.697 (+/-0.303) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.695 (+/-0.299) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.694 (+/-0.299) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.693 (+/-0.300) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.694 (+/-0.304) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.693 (+/-0.303) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.690 (+/-0.302) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.699 (+/-0.344) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.656 (+/-0.311) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.656 (+/-0.311) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.656 (+/-0.311) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.696 (+/-0.301) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.694 (+/-0.297) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.694 (+/-0.299) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.693 (+/-0.300) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.693 (+/-0.303) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.692 (+/-0.304) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.690 (+/-0.303) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.699 (+/-0.344) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.656 (+/-0.311) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.656 (+/-0.312) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.656 (+/-0.311) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.696 (+/-0.300) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.693 (+/-0.297) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.694 (+/-0.299) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.693 (+/-0.300) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.693 (+/-0.303) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.717 (+/-0.352) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.714 (+/-0.349) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.682 (+/-0.365) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.656 (+/-0.312) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.656 (+/-0.312) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.656 (+/-0.311) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.695 (+/-0.299) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.693 (+/-0.296) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.694 (+/-0.299) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.693 (+/-0.300) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.693 (+/-0.304) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.717 (+/-0.351) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.714 (+/-0.349) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.682 (+/-0.365) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.656 (+/-0.313) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.656 (+/-0.312) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.656 (+/-0.311) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.695 (+/-0.298) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.692 (+/-0.295) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.693 (+/-0.299) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.693 (+/-0.301) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.693 (+/-0.303) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.717 (+/-0.352) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.714 (+/-0.349) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.682 (+/-0.366) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.656 (+/-0.314) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.656 (+/-0.312) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.656 (+/-0.311) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.694 (+/-0.297) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.692 (+/-0.296) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.693 (+/-0.299) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.693 (+/-0.301) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.693 (+/-0.303) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.716 (+/-0.353) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.697 (+/-0.342) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.681 (+/-0.365) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.656 (+/-0.313) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.656 (+/-0.311) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.656 (+/-0.311) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.693 (+/-0.296) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.692 (+/-0.296) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.693 (+/-0.300) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.694 (+/-0.303) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.693 (+/-0.303) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.716 (+/-0.352) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.697 (+/-0.342) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.657 (+/-0.312) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.656 (+/-0.313) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.656 (+/-0.311) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.656 (+/-0.311) for {'C': 1737800.828749377, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.693 (+/-0.296) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.692 (+/-0.296) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.694 (+/-0.301) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.694 (+/-0.303) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.692 (+/-0.303) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.716 (+/-0.352) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.680 (+/-0.362) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.656 (+/-0.312) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.656 (+/-0.313) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.656 (+/-0.311) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.656 (+/-0.311) for {'C': 1905460.71796325, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.693 (+/-0.296) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.692 (+/-0.297) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.694 (+/-0.302) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.694 (+/-0.303) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.692 (+/-0.303) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.716 (+/-0.351) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.680 (+/-0.364) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.656 (+/-0.312) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.656 (+/-0.312) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.656 (+/-0.310) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.656 (+/-0.311) for {'C': 2089296.1308540385, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.693 (+/-0.297) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.692 (+/-0.297) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.694 (+/-0.302) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.694 (+/-0.304) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.692 (+/-0.304) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.716 (+/-0.351) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.680 (+/-0.364) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.656 (+/-0.312) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.656 (+/-0.312) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.656 (+/-0.310) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.656 (+/-0.310) for {'C': 2290867.6527677733, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.692 (+/-0.297) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.693 (+/-0.297) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.694 (+/-0.303) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.693 (+/-0.304) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.691 (+/-0.304) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.699 (+/-0.344) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.655 (+/-0.310) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.656 (+/-0.312) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.656 (+/-0.312) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.656 (+/-0.310) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.657 (+/-0.310) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.700 (+/-0.309) for {'C': 0.1, 'kernel': 'linear'}
0.698 (+/-0.306) for {'C': 1.0, 'kernel': 'linear'}
0.694 (+/-0.301) for {'C': 10.0, 'kernel': 'linear'}
0.657 (+/-0.313) for {'C': 100.0, 'kernel': 'linear'}
0.659 (+/-0.314) for {'C': 1000.0, 'kernel': 'linear'}
0.634 (+/-0.324) for {'C': 10000.0, 'kernel': 'linear'}
0.633 (+/-0.324) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.04 0.17 0.06 6
1 0.99 0.96 0.98 623
avg / total 0.98 0.95 0.97 629
本轮grid search结果,得到最好的参数选择是: {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.717144344133894
第0轮loop中,tunemodel函数得到的最优参数是: ([{'C': array([1096478.19614319, 1116863.24778056, 1137627.28582343,
1158777.35615513, 1180320.63565173, 1202264.43461741,
1224616.19926505, 1247383.51424294, 1270574.10520854,
1294195.84144999, 1318256.73855641]), 'kernel': ['rbf'], 'gamma': array([6.30957344e-06, 6.91830971e-06, 7.58577575e-06, 8.31763771e-06,
9.12010839e-06, 1.00000000e-05, 1.09647820e-05, 1.20226443e-05,
1.31825674e-05, 1.44543977e-05, 1.58489319e-05])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}], {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}, 0.717144344133894)
这是第0次迭代微调C和gamma。
第0次迭代,得到delta: [3.82628758e+05 5.08219768e-21]
执行tunemodel()函数前,使用的grid search parameter是:
[{'C': array([1096478.19614319, 1116863.24778056, 1137627.28582343,
1158777.35615513, 1180320.63565173, 1202264.43461741,
1224616.19926505, 1247383.51424294, 1270574.10520854,
1294195.84144999, 1318256.73855641]), 'kernel': ['rbf'], 'gamma': array([6.30957344e-06, 6.91830971e-06, 7.58577575e-06, 8.31763771e-06,
9.12010839e-06, 1.00000000e-05, 1.09647820e-05, 1.20226443e-05,
1.31825674e-05, 1.44543977e-05, 1.58489319e-05])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.590 (+/-0.154) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.601 (+/-0.181) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.603 (+/-0.182) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.593 (+/-0.157) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 8.317637711026698e-06}
0.604 (+/-0.182) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.602 (+/-0.182) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.590 (+/-0.154) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 1.0964781961431852e-05}
0.599 (+/-0.164) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 1.2022644346174139e-05}
0.597 (+/-0.158) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 1.3182567385564057e-05}
0.601 (+/-0.159) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 1.4454397707459268e-05}
0.594 (+/-0.159) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.590 (+/-0.154) for {'C': 1116863.2477805612, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.601 (+/-0.181) for {'C': 1116863.2477805612, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.595 (+/-0.161) for {'C': 1116863.2477805612, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.593 (+/-0.157) for {'C': 1116863.2477805612, 'kernel': 'rbf', 'gamma': 8.317637711026698e-06}
0.604 (+/-0.182) for {'C': 1116863.2477805612, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.602 (+/-0.182) for {'C': 1116863.2477805612, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.590 (+/-0.154) for {'C': 1116863.2477805612, 'kernel': 'rbf', 'gamma': 1.0964781961431852e-05}
0.600 (+/-0.164) for {'C': 1116863.2477805612, 'kernel': 'rbf', 'gamma': 1.2022644346174139e-05}
0.597 (+/-0.158) for {'C': 1116863.2477805612, 'kernel': 'rbf', 'gamma': 1.3182567385564057e-05}
0.599 (+/-0.158) for {'C': 1116863.2477805612, 'kernel': 'rbf', 'gamma': 1.4454397707459268e-05}
0.594 (+/-0.159) for {'C': 1116863.2477805612, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.588 (+/-0.154) for {'C': 1137627.2858234306, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.601 (+/-0.181) for {'C': 1137627.2858234306, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.595 (+/-0.161) for {'C': 1137627.2858234306, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.593 (+/-0.157) for {'C': 1137627.2858234306, 'kernel': 'rbf', 'gamma': 8.317637711026698e-06}
0.602 (+/-0.181) for {'C': 1137627.2858234306, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.602 (+/-0.182) for {'C': 1137627.2858234306, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.590 (+/-0.154) for {'C': 1137627.2858234306, 'kernel': 'rbf', 'gamma': 1.0964781961431852e-05}
0.600 (+/-0.164) for {'C': 1137627.2858234306, 'kernel': 'rbf', 'gamma': 1.2022644346174139e-05}
0.597 (+/-0.158) for {'C': 1137627.2858234306, 'kernel': 'rbf', 'gamma': 1.3182567385564057e-05}
0.599 (+/-0.158) for {'C': 1137627.2858234306, 'kernel': 'rbf', 'gamma': 1.4454397707459268e-05}
0.598 (+/-0.164) for {'C': 1137627.2858234306, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.588 (+/-0.154) for {'C': 1158777.3561551254, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.601 (+/-0.181) for {'C': 1158777.3561551254, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.595 (+/-0.161) for {'C': 1158777.3561551254, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.589 (+/-0.152) for {'C': 1158777.3561551254, 'kernel': 'rbf', 'gamma': 8.317637711026698e-06}
0.606 (+/-0.183) for {'C': 1158777.3561551254, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.594 (+/-0.161) for {'C': 1158777.3561551254, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.590 (+/-0.154) for {'C': 1158777.3561551254, 'kernel': 'rbf', 'gamma': 1.0964781961431852e-05}
0.600 (+/-0.164) for {'C': 1158777.3561551254, 'kernel': 'rbf', 'gamma': 1.2022644346174139e-05}
0.597 (+/-0.158) for {'C': 1158777.3561551254, 'kernel': 'rbf', 'gamma': 1.3182567385564057e-05}
0.599 (+/-0.158) for {'C': 1158777.3561551254, 'kernel': 'rbf', 'gamma': 1.4454397707459268e-05}
0.598 (+/-0.164) for {'C': 1158777.3561551254, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.588 (+/-0.154) for {'C': 1180320.6356517284, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.601 (+/-0.181) for {'C': 1180320.6356517284, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.595 (+/-0.161) for {'C': 1180320.6356517284, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.587 (+/-0.150) for {'C': 1180320.6356517284, 'kernel': 'rbf', 'gamma': 8.317637711026698e-06}
0.602 (+/-0.181) for {'C': 1180320.6356517284, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.594 (+/-0.161) for {'C': 1180320.6356517284, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.590 (+/-0.154) for {'C': 1180320.6356517284, 'kernel': 'rbf', 'gamma': 1.0964781961431852e-05}
0.603 (+/-0.164) for {'C': 1180320.6356517284, 'kernel': 'rbf', 'gamma': 1.2022644346174139e-05}
0.597 (+/-0.158) for {'C': 1180320.6356517284, 'kernel': 'rbf', 'gamma': 1.3182567385564057e-05}
0.599 (+/-0.158) for {'C': 1180320.6356517284, 'kernel': 'rbf', 'gamma': 1.4454397707459268e-05}
0.598 (+/-0.164) for {'C': 1180320.6356517284, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.596 (+/-0.177) for {'C': 1202264.4346174113, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.601 (+/-0.181) for {'C': 1202264.4346174113, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.592 (+/-0.155) for {'C': 1202264.4346174113, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.587 (+/-0.150) for {'C': 1202264.4346174113, 'kernel': 'rbf', 'gamma': 8.317637711026698e-06}
0.606 (+/-0.183) for {'C': 1202264.4346174113, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.594 (+/-0.161) for {'C': 1202264.4346174113, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.590 (+/-0.154) for {'C': 1202264.4346174113, 'kernel': 'rbf', 'gamma': 1.0964781961431852e-05}
0.603 (+/-0.164) for {'C': 1202264.4346174113, 'kernel': 'rbf', 'gamma': 1.2022644346174139e-05}
0.597 (+/-0.158) for {'C': 1202264.4346174113, 'kernel': 'rbf', 'gamma': 1.3182567385564057e-05}
0.599 (+/-0.158) for {'C': 1202264.4346174113, 'kernel': 'rbf', 'gamma': 1.4454397707459268e-05}
0.598 (+/-0.164) for {'C': 1202264.4346174113, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.596 (+/-0.177) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.600 (+/-0.181) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.592 (+/-0.155) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.591 (+/-0.156) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 8.317637711026698e-06}
0.607 (+/-0.182) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.594 (+/-0.161) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.590 (+/-0.154) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 1.0964781961431852e-05}
0.603 (+/-0.164) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 1.2022644346174139e-05}
0.597 (+/-0.158) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 1.3182567385564057e-05}
0.599 (+/-0.158) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 1.4454397707459268e-05}
0.598 (+/-0.164) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.596 (+/-0.177) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.601 (+/-0.181) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.604 (+/-0.182) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.591 (+/-0.156) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 8.317637711026698e-06}
0.607 (+/-0.182) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.594 (+/-0.161) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.590 (+/-0.154) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 1.0964781961431852e-05}
0.601 (+/-0.165) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 1.2022644346174139e-05}
0.597 (+/-0.158) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 1.3182567385564057e-05}
0.599 (+/-0.158) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 1.4454397707459268e-05}
0.598 (+/-0.164) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.596 (+/-0.177) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.603 (+/-0.182) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.595 (+/-0.161) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.593 (+/-0.155) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 8.317637711026698e-06}
0.607 (+/-0.182) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.594 (+/-0.161) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.590 (+/-0.154) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 1.0964781961431852e-05}
0.603 (+/-0.164) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 1.2022644346174139e-05}
0.597 (+/-0.158) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 1.3182567385564057e-05}
0.599 (+/-0.158) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 1.4454397707459268e-05}
0.598 (+/-0.164) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.596 (+/-0.177) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.603 (+/-0.182) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.591 (+/-0.156) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.597 (+/-0.161) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 8.317637711026698e-06}
0.607 (+/-0.182) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.593 (+/-0.162) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.590 (+/-0.154) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 1.0964781961431852e-05}
0.604 (+/-0.164) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 1.2022644346174139e-05}
0.601 (+/-0.163) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 1.3182567385564057e-05}
0.599 (+/-0.158) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 1.4454397707459268e-05}
0.598 (+/-0.164) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.596 (+/-0.177) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.601 (+/-0.181) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.592 (+/-0.156) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.597 (+/-0.161) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 8.317637711026698e-06}
0.607 (+/-0.182) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.593 (+/-0.162) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.590 (+/-0.154) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.0964781961431852e-05}
0.604 (+/-0.164) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.2022644346174139e-05}
0.601 (+/-0.163) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.3182567385564057e-05}
0.599 (+/-0.158) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.4454397707459268e-05}
0.599 (+/-0.164) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.685 (+/-0.297) for {'C': 1.0, 'kernel': 'linear'}
0.604 (+/-0.181) for {'C': 10.0, 'kernel': 'linear'}
0.609 (+/-0.201) for {'C': 100.0, 'kernel': 'linear'}
0.584 (+/-0.186) for {'C': 1000.0, 'kernel': 'linear'}
0.577 (+/-0.192) for {'C': 10000.0, 'kernel': 'linear'}
0.576 (+/-0.191) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.695 (+/-0.301) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.695 (+/-0.302) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.695 (+/-0.301) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.695 (+/-0.303) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 8.317637711026698e-06}
0.695 (+/-0.303) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.695 (+/-0.300) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.695 (+/-0.300) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 1.0964781961431852e-05}
0.695 (+/-0.301) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 1.2022644346174139e-05}
0.695 (+/-0.302) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 1.3182567385564057e-05}
0.696 (+/-0.302) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 1.4454397707459268e-05}
0.695 (+/-0.300) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.695 (+/-0.301) for {'C': 1116863.2477805612, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.695 (+/-0.302) for {'C': 1116863.2477805612, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.695 (+/-0.301) for {'C': 1116863.2477805612, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.695 (+/-0.303) for {'C': 1116863.2477805612, 'kernel': 'rbf', 'gamma': 8.317637711026698e-06}
0.695 (+/-0.303) for {'C': 1116863.2477805612, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.695 (+/-0.300) for {'C': 1116863.2477805612, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.695 (+/-0.300) for {'C': 1116863.2477805612, 'kernel': 'rbf', 'gamma': 1.0964781961431852e-05}
0.696 (+/-0.302) for {'C': 1116863.2477805612, 'kernel': 'rbf', 'gamma': 1.2022644346174139e-05}
0.695 (+/-0.302) for {'C': 1116863.2477805612, 'kernel': 'rbf', 'gamma': 1.3182567385564057e-05}
0.696 (+/-0.302) for {'C': 1116863.2477805612, 'kernel': 'rbf', 'gamma': 1.4454397707459268e-05}
0.695 (+/-0.300) for {'C': 1116863.2477805612, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.694 (+/-0.299) for {'C': 1137627.2858234306, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.695 (+/-0.302) for {'C': 1137627.2858234306, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.695 (+/-0.301) for {'C': 1137627.2858234306, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.695 (+/-0.303) for {'C': 1137627.2858234306, 'kernel': 'rbf', 'gamma': 8.317637711026698e-06}
0.695 (+/-0.302) for {'C': 1137627.2858234306, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.695 (+/-0.300) for {'C': 1137627.2858234306, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.695 (+/-0.300) for {'C': 1137627.2858234306, 'kernel': 'rbf', 'gamma': 1.0964781961431852e-05}
0.696 (+/-0.302) for {'C': 1137627.2858234306, 'kernel': 'rbf', 'gamma': 1.2022644346174139e-05}
0.695 (+/-0.303) for {'C': 1137627.2858234306, 'kernel': 'rbf', 'gamma': 1.3182567385564057e-05}
0.696 (+/-0.302) for {'C': 1137627.2858234306, 'kernel': 'rbf', 'gamma': 1.4454397707459268e-05}
0.695 (+/-0.300) for {'C': 1137627.2858234306, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.694 (+/-0.299) for {'C': 1158777.3561551254, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.695 (+/-0.302) for {'C': 1158777.3561551254, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.695 (+/-0.301) for {'C': 1158777.3561551254, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.695 (+/-0.303) for {'C': 1158777.3561551254, 'kernel': 'rbf', 'gamma': 8.317637711026698e-06}
0.695 (+/-0.304) for {'C': 1158777.3561551254, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.695 (+/-0.300) for {'C': 1158777.3561551254, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.695 (+/-0.300) for {'C': 1158777.3561551254, 'kernel': 'rbf', 'gamma': 1.0964781961431852e-05}
0.696 (+/-0.302) for {'C': 1158777.3561551254, 'kernel': 'rbf', 'gamma': 1.2022644346174139e-05}
0.695 (+/-0.303) for {'C': 1158777.3561551254, 'kernel': 'rbf', 'gamma': 1.3182567385564057e-05}
0.696 (+/-0.302) for {'C': 1158777.3561551254, 'kernel': 'rbf', 'gamma': 1.4454397707459268e-05}
0.695 (+/-0.300) for {'C': 1158777.3561551254, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.694 (+/-0.299) for {'C': 1180320.6356517284, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.695 (+/-0.302) for {'C': 1180320.6356517284, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.695 (+/-0.301) for {'C': 1180320.6356517284, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.695 (+/-0.302) for {'C': 1180320.6356517284, 'kernel': 'rbf', 'gamma': 8.317637711026698e-06}
0.695 (+/-0.302) for {'C': 1180320.6356517284, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.695 (+/-0.300) for {'C': 1180320.6356517284, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.695 (+/-0.300) for {'C': 1180320.6356517284, 'kernel': 'rbf', 'gamma': 1.0964781961431852e-05}
0.696 (+/-0.302) for {'C': 1180320.6356517284, 'kernel': 'rbf', 'gamma': 1.2022644346174139e-05}
0.695 (+/-0.303) for {'C': 1180320.6356517284, 'kernel': 'rbf', 'gamma': 1.3182567385564057e-05}
0.696 (+/-0.302) for {'C': 1180320.6356517284, 'kernel': 'rbf', 'gamma': 1.4454397707459268e-05}
0.695 (+/-0.300) for {'C': 1180320.6356517284, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.695 (+/-0.300) for {'C': 1202264.4346174113, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.695 (+/-0.302) for {'C': 1202264.4346174113, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.695 (+/-0.301) for {'C': 1202264.4346174113, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.695 (+/-0.302) for {'C': 1202264.4346174113, 'kernel': 'rbf', 'gamma': 8.317637711026698e-06}
0.695 (+/-0.304) for {'C': 1202264.4346174113, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.695 (+/-0.300) for {'C': 1202264.4346174113, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.695 (+/-0.300) for {'C': 1202264.4346174113, 'kernel': 'rbf', 'gamma': 1.0964781961431852e-05}
0.696 (+/-0.302) for {'C': 1202264.4346174113, 'kernel': 'rbf', 'gamma': 1.2022644346174139e-05}
0.695 (+/-0.303) for {'C': 1202264.4346174113, 'kernel': 'rbf', 'gamma': 1.3182567385564057e-05}
0.696 (+/-0.302) for {'C': 1202264.4346174113, 'kernel': 'rbf', 'gamma': 1.4454397707459268e-05}
0.695 (+/-0.300) for {'C': 1202264.4346174113, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.694 (+/-0.299) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.695 (+/-0.302) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.695 (+/-0.300) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.695 (+/-0.302) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 8.317637711026698e-06}
0.696 (+/-0.304) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.695 (+/-0.300) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.695 (+/-0.300) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 1.0964781961431852e-05}
0.696 (+/-0.302) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 1.2022644346174139e-05}
0.695 (+/-0.303) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 1.3182567385564057e-05}
0.696 (+/-0.302) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 1.4454397707459268e-05}
0.695 (+/-0.301) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.694 (+/-0.299) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.695 (+/-0.302) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.695 (+/-0.300) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.695 (+/-0.302) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 8.317637711026698e-06}
0.696 (+/-0.304) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.695 (+/-0.300) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.695 (+/-0.300) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 1.0964781961431852e-05}
0.695 (+/-0.301) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 1.2022644346174139e-05}
0.695 (+/-0.303) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 1.3182567385564057e-05}
0.696 (+/-0.302) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 1.4454397707459268e-05}
0.695 (+/-0.301) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.694 (+/-0.299) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.695 (+/-0.302) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.695 (+/-0.301) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.695 (+/-0.303) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 8.317637711026698e-06}
0.696 (+/-0.304) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.695 (+/-0.300) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.695 (+/-0.300) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 1.0964781961431852e-05}
0.696 (+/-0.302) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 1.2022644346174139e-05}
0.695 (+/-0.303) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 1.3182567385564057e-05}
0.696 (+/-0.302) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 1.4454397707459268e-05}
0.695 (+/-0.301) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.694 (+/-0.299) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.695 (+/-0.302) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.695 (+/-0.300) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.695 (+/-0.304) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 8.317637711026698e-06}
0.696 (+/-0.304) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.695 (+/-0.299) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.695 (+/-0.300) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 1.0964781961431852e-05}
0.696 (+/-0.303) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 1.2022644346174139e-05}
0.695 (+/-0.303) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 1.3182567385564057e-05}
0.696 (+/-0.302) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 1.4454397707459268e-05}
0.695 (+/-0.301) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.694 (+/-0.299) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.695 (+/-0.302) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.695 (+/-0.301) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.695 (+/-0.304) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 8.317637711026698e-06}
0.696 (+/-0.304) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.694 (+/-0.300) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.695 (+/-0.300) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.0964781961431852e-05}
0.696 (+/-0.303) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.2022644346174139e-05}
0.695 (+/-0.303) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.3182567385564057e-05}
0.696 (+/-0.302) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.4454397707459268e-05}
0.695 (+/-0.301) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.698 (+/-0.306) for {'C': 1.0, 'kernel': 'linear'}
0.696 (+/-0.302) for {'C': 10.0, 'kernel': 'linear'}
0.663 (+/-0.315) for {'C': 100.0, 'kernel': 'linear'}
0.660 (+/-0.314) for {'C': 1000.0, 'kernel': 'linear'}
0.635 (+/-0.324) for {'C': 10000.0, 'kernel': 'linear'}
0.634 (+/-0.325) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6983958900156649
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.581 (+/-0.146) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.582 (+/-0.152) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.585 (+/-0.154) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.578 (+/-0.146) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 8.317637711026698e-06}
0.584 (+/-0.154) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.584 (+/-0.154) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.581 (+/-0.154) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 1.0964781961431852e-05}
0.587 (+/-0.159) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 1.2022644346174139e-05}
0.586 (+/-0.161) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 1.3182567385564057e-05}
0.590 (+/-0.163) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 1.4454397707459268e-05}
0.582 (+/-0.163) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.582 (+/-0.145) for {'C': 1116863.2477805612, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.582 (+/-0.152) for {'C': 1116863.2477805612, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.584 (+/-0.154) for {'C': 1116863.2477805612, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.578 (+/-0.146) for {'C': 1116863.2477805612, 'kernel': 'rbf', 'gamma': 8.317637711026698e-06}
0.584 (+/-0.154) for {'C': 1116863.2477805612, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.583 (+/-0.155) for {'C': 1116863.2477805612, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.581 (+/-0.154) for {'C': 1116863.2477805612, 'kernel': 'rbf', 'gamma': 1.0964781961431852e-05}
0.587 (+/-0.159) for {'C': 1116863.2477805612, 'kernel': 'rbf', 'gamma': 1.2022644346174139e-05}
0.590 (+/-0.158) for {'C': 1116863.2477805612, 'kernel': 'rbf', 'gamma': 1.3182567385564057e-05}
0.587 (+/-0.162) for {'C': 1116863.2477805612, 'kernel': 'rbf', 'gamma': 1.4454397707459268e-05}
0.582 (+/-0.163) for {'C': 1116863.2477805612, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.581 (+/-0.146) for {'C': 1137627.2858234306, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.582 (+/-0.152) for {'C': 1137627.2858234306, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.584 (+/-0.155) for {'C': 1137627.2858234306, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.578 (+/-0.146) for {'C': 1137627.2858234306, 'kernel': 'rbf', 'gamma': 8.317637711026698e-06}
0.584 (+/-0.154) for {'C': 1137627.2858234306, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.583 (+/-0.155) for {'C': 1137627.2858234306, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.581 (+/-0.155) for {'C': 1137627.2858234306, 'kernel': 'rbf', 'gamma': 1.0964781961431852e-05}
0.587 (+/-0.160) for {'C': 1137627.2858234306, 'kernel': 'rbf', 'gamma': 1.2022644346174139e-05}
0.590 (+/-0.158) for {'C': 1137627.2858234306, 'kernel': 'rbf', 'gamma': 1.3182567385564057e-05}
0.587 (+/-0.162) for {'C': 1137627.2858234306, 'kernel': 'rbf', 'gamma': 1.4454397707459268e-05}
0.582 (+/-0.163) for {'C': 1137627.2858234306, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.580 (+/-0.145) for {'C': 1158777.3561551254, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.582 (+/-0.152) for {'C': 1158777.3561551254, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.584 (+/-0.154) for {'C': 1158777.3561551254, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.578 (+/-0.146) for {'C': 1158777.3561551254, 'kernel': 'rbf', 'gamma': 8.317637711026698e-06}
0.584 (+/-0.154) for {'C': 1158777.3561551254, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.583 (+/-0.155) for {'C': 1158777.3561551254, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.580 (+/-0.155) for {'C': 1158777.3561551254, 'kernel': 'rbf', 'gamma': 1.0964781961431852e-05}
0.587 (+/-0.160) for {'C': 1158777.3561551254, 'kernel': 'rbf', 'gamma': 1.2022644346174139e-05}
0.589 (+/-0.159) for {'C': 1158777.3561551254, 'kernel': 'rbf', 'gamma': 1.3182567385564057e-05}
0.587 (+/-0.162) for {'C': 1158777.3561551254, 'kernel': 'rbf', 'gamma': 1.4454397707459268e-05}
0.585 (+/-0.160) for {'C': 1158777.3561551254, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.580 (+/-0.145) for {'C': 1180320.6356517284, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.582 (+/-0.152) for {'C': 1180320.6356517284, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.584 (+/-0.154) for {'C': 1180320.6356517284, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.578 (+/-0.146) for {'C': 1180320.6356517284, 'kernel': 'rbf', 'gamma': 8.317637711026698e-06}
0.584 (+/-0.154) for {'C': 1180320.6356517284, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.583 (+/-0.155) for {'C': 1180320.6356517284, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.580 (+/-0.155) for {'C': 1180320.6356517284, 'kernel': 'rbf', 'gamma': 1.0964781961431852e-05}
0.589 (+/-0.161) for {'C': 1180320.6356517284, 'kernel': 'rbf', 'gamma': 1.2022644346174139e-05}
0.589 (+/-0.159) for {'C': 1180320.6356517284, 'kernel': 'rbf', 'gamma': 1.3182567385564057e-05}
0.587 (+/-0.162) for {'C': 1180320.6356517284, 'kernel': 'rbf', 'gamma': 1.4454397707459268e-05}
0.585 (+/-0.160) for {'C': 1180320.6356517284, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.580 (+/-0.145) for {'C': 1202264.4346174113, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.582 (+/-0.152) for {'C': 1202264.4346174113, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.585 (+/-0.154) for {'C': 1202264.4346174113, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.578 (+/-0.146) for {'C': 1202264.4346174113, 'kernel': 'rbf', 'gamma': 8.317637711026698e-06}
0.584 (+/-0.154) for {'C': 1202264.4346174113, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.587 (+/-0.154) for {'C': 1202264.4346174113, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.580 (+/-0.155) for {'C': 1202264.4346174113, 'kernel': 'rbf', 'gamma': 1.0964781961431852e-05}
0.589 (+/-0.162) for {'C': 1202264.4346174113, 'kernel': 'rbf', 'gamma': 1.2022644346174139e-05}
0.590 (+/-0.158) for {'C': 1202264.4346174113, 'kernel': 'rbf', 'gamma': 1.3182567385564057e-05}
0.587 (+/-0.162) for {'C': 1202264.4346174113, 'kernel': 'rbf', 'gamma': 1.4454397707459268e-05}
0.585 (+/-0.160) for {'C': 1202264.4346174113, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.580 (+/-0.145) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.580 (+/-0.151) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.587 (+/-0.154) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.578 (+/-0.146) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 8.317637711026698e-06}
0.585 (+/-0.154) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.587 (+/-0.155) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.580 (+/-0.155) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 1.0964781961431852e-05}
0.589 (+/-0.162) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 1.2022644346174139e-05}
0.589 (+/-0.159) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 1.3182567385564057e-05}
0.587 (+/-0.162) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 1.4454397707459268e-05}
0.585 (+/-0.161) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.580 (+/-0.145) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.581 (+/-0.153) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.585 (+/-0.155) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.578 (+/-0.146) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 8.317637711026698e-06}
0.585 (+/-0.154) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.586 (+/-0.154) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.579 (+/-0.155) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 1.0964781961431852e-05}
0.589 (+/-0.162) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 1.2022644346174139e-05}
0.589 (+/-0.159) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 1.3182567385564057e-05}
0.587 (+/-0.162) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 1.4454397707459268e-05}
0.585 (+/-0.161) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.580 (+/-0.145) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.583 (+/-0.155) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.585 (+/-0.155) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.578 (+/-0.146) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 8.317637711026698e-06}
0.585 (+/-0.154) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.586 (+/-0.154) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.579 (+/-0.155) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 1.0964781961431852e-05}
0.588 (+/-0.162) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 1.2022644346174139e-05}
0.588 (+/-0.159) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 1.3182567385564057e-05}
0.587 (+/-0.162) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 1.4454397707459268e-05}
0.585 (+/-0.161) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.580 (+/-0.145) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.583 (+/-0.155) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.584 (+/-0.155) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.578 (+/-0.146) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 8.317637711026698e-06}
0.585 (+/-0.154) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.586 (+/-0.154) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.579 (+/-0.155) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 1.0964781961431852e-05}
0.588 (+/-0.162) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 1.2022644346174139e-05}
0.588 (+/-0.159) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 1.3182567385564057e-05}
0.587 (+/-0.162) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 1.4454397707459268e-05}
0.585 (+/-0.161) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.580 (+/-0.145) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.581 (+/-0.153) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.583 (+/-0.156) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.577 (+/-0.146) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 8.317637711026698e-06}
0.585 (+/-0.154) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.586 (+/-0.154) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.579 (+/-0.155) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.0964781961431852e-05}
0.588 (+/-0.162) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.2022644346174139e-05}
0.588 (+/-0.159) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.3182567385564057e-05}
0.586 (+/-0.162) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.4454397707459268e-05}
0.586 (+/-0.161) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.798 (+/-0.437) for {'C': 0.1, 'kernel': 'linear'}
0.660 (+/-0.219) for {'C': 1.0, 'kernel': 'linear'}
0.585 (+/-0.147) for {'C': 10.0, 'kernel': 'linear'}
0.582 (+/-0.190) for {'C': 100.0, 'kernel': 'linear'}
0.581 (+/-0.185) for {'C': 1000.0, 'kernel': 'linear'}
0.576 (+/-0.191) for {'C': 10000.0, 'kernel': 'linear'}
0.575 (+/-0.191) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.693 (+/-0.303) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.693 (+/-0.303) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.693 (+/-0.303) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.693 (+/-0.304) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 8.317637711026698e-06}
0.693 (+/-0.304) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.692 (+/-0.304) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.692 (+/-0.302) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 1.0964781961431852e-05}
0.693 (+/-0.304) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 1.2022644346174139e-05}
0.692 (+/-0.303) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 1.3182567385564057e-05}
0.692 (+/-0.305) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 1.4454397707459268e-05}
0.690 (+/-0.303) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.693 (+/-0.304) for {'C': 1116863.2477805612, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.693 (+/-0.303) for {'C': 1116863.2477805612, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.693 (+/-0.303) for {'C': 1116863.2477805612, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.693 (+/-0.304) for {'C': 1116863.2477805612, 'kernel': 'rbf', 'gamma': 8.317637711026698e-06}
0.693 (+/-0.304) for {'C': 1116863.2477805612, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.692 (+/-0.304) for {'C': 1116863.2477805612, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.692 (+/-0.302) for {'C': 1116863.2477805612, 'kernel': 'rbf', 'gamma': 1.0964781961431852e-05}
0.692 (+/-0.304) for {'C': 1116863.2477805612, 'kernel': 'rbf', 'gamma': 1.2022644346174139e-05}
0.717 (+/-0.351) for {'C': 1116863.2477805612, 'kernel': 'rbf', 'gamma': 1.3182567385564057e-05}
0.691 (+/-0.305) for {'C': 1116863.2477805612, 'kernel': 'rbf', 'gamma': 1.4454397707459268e-05}
0.690 (+/-0.303) for {'C': 1116863.2477805612, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.693 (+/-0.304) for {'C': 1137627.2858234306, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.693 (+/-0.303) for {'C': 1137627.2858234306, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.692 (+/-0.303) for {'C': 1137627.2858234306, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.693 (+/-0.304) for {'C': 1137627.2858234306, 'kernel': 'rbf', 'gamma': 8.317637711026698e-06}
0.693 (+/-0.304) for {'C': 1137627.2858234306, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.692 (+/-0.303) for {'C': 1137627.2858234306, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.692 (+/-0.302) for {'C': 1137627.2858234306, 'kernel': 'rbf', 'gamma': 1.0964781961431852e-05}
0.692 (+/-0.304) for {'C': 1137627.2858234306, 'kernel': 'rbf', 'gamma': 1.2022644346174139e-05}
0.717 (+/-0.351) for {'C': 1137627.2858234306, 'kernel': 'rbf', 'gamma': 1.3182567385564057e-05}
0.691 (+/-0.305) for {'C': 1137627.2858234306, 'kernel': 'rbf', 'gamma': 1.4454397707459268e-05}
0.690 (+/-0.303) for {'C': 1137627.2858234306, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.693 (+/-0.303) for {'C': 1158777.3561551254, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.693 (+/-0.303) for {'C': 1158777.3561551254, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.693 (+/-0.303) for {'C': 1158777.3561551254, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.693 (+/-0.304) for {'C': 1158777.3561551254, 'kernel': 'rbf', 'gamma': 8.317637711026698e-06}
0.693 (+/-0.304) for {'C': 1158777.3561551254, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.692 (+/-0.303) for {'C': 1158777.3561551254, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.692 (+/-0.301) for {'C': 1158777.3561551254, 'kernel': 'rbf', 'gamma': 1.0964781961431852e-05}
0.692 (+/-0.304) for {'C': 1158777.3561551254, 'kernel': 'rbf', 'gamma': 1.2022644346174139e-05}
0.716 (+/-0.351) for {'C': 1158777.3561551254, 'kernel': 'rbf', 'gamma': 1.3182567385564057e-05}
0.691 (+/-0.305) for {'C': 1158777.3561551254, 'kernel': 'rbf', 'gamma': 1.4454397707459268e-05}
0.715 (+/-0.348) for {'C': 1158777.3561551254, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.693 (+/-0.303) for {'C': 1180320.6356517284, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.693 (+/-0.303) for {'C': 1180320.6356517284, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.693 (+/-0.303) for {'C': 1180320.6356517284, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.693 (+/-0.304) for {'C': 1180320.6356517284, 'kernel': 'rbf', 'gamma': 8.317637711026698e-06}
0.693 (+/-0.305) for {'C': 1180320.6356517284, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.692 (+/-0.303) for {'C': 1180320.6356517284, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.692 (+/-0.301) for {'C': 1180320.6356517284, 'kernel': 'rbf', 'gamma': 1.0964781961431852e-05}
0.692 (+/-0.304) for {'C': 1180320.6356517284, 'kernel': 'rbf', 'gamma': 1.2022644346174139e-05}
0.716 (+/-0.351) for {'C': 1180320.6356517284, 'kernel': 'rbf', 'gamma': 1.3182567385564057e-05}
0.691 (+/-0.305) for {'C': 1180320.6356517284, 'kernel': 'rbf', 'gamma': 1.4454397707459268e-05}
0.714 (+/-0.349) for {'C': 1180320.6356517284, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.693 (+/-0.303) for {'C': 1202264.4346174113, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.693 (+/-0.303) for {'C': 1202264.4346174113, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.693 (+/-0.303) for {'C': 1202264.4346174113, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.692 (+/-0.303) for {'C': 1202264.4346174113, 'kernel': 'rbf', 'gamma': 8.317637711026698e-06}
0.693 (+/-0.305) for {'C': 1202264.4346174113, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.717 (+/-0.352) for {'C': 1202264.4346174113, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.691 (+/-0.301) for {'C': 1202264.4346174113, 'kernel': 'rbf', 'gamma': 1.0964781961431852e-05}
0.692 (+/-0.304) for {'C': 1202264.4346174113, 'kernel': 'rbf', 'gamma': 1.2022644346174139e-05}
0.717 (+/-0.351) for {'C': 1202264.4346174113, 'kernel': 'rbf', 'gamma': 1.3182567385564057e-05}
0.691 (+/-0.305) for {'C': 1202264.4346174113, 'kernel': 'rbf', 'gamma': 1.4454397707459268e-05}
0.714 (+/-0.349) for {'C': 1202264.4346174113, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.693 (+/-0.303) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.692 (+/-0.303) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.693 (+/-0.303) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.692 (+/-0.303) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 8.317637711026698e-06}
0.693 (+/-0.305) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.717 (+/-0.352) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.691 (+/-0.301) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 1.0964781961431852e-05}
0.692 (+/-0.304) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 1.2022644346174139e-05}
0.716 (+/-0.351) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 1.3182567385564057e-05}
0.691 (+/-0.305) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 1.4454397707459268e-05}
0.714 (+/-0.349) for {'C': 1224616.1992650495, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.693 (+/-0.304) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.692 (+/-0.303) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.693 (+/-0.303) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.692 (+/-0.303) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 8.317637711026698e-06}
0.693 (+/-0.305) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.717 (+/-0.351) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.691 (+/-0.301) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 1.0964781961431852e-05}
0.692 (+/-0.304) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 1.2022644346174139e-05}
0.716 (+/-0.351) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 1.3182567385564057e-05}
0.691 (+/-0.306) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 1.4454397707459268e-05}
0.714 (+/-0.349) for {'C': 1247383.5142429434, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.693 (+/-0.304) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.692 (+/-0.304) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.693 (+/-0.303) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.692 (+/-0.304) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 8.317637711026698e-06}
0.693 (+/-0.305) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.717 (+/-0.351) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.691 (+/-0.301) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 1.0964781961431852e-05}
0.692 (+/-0.303) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 1.2022644346174139e-05}
0.716 (+/-0.350) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 1.3182567385564057e-05}
0.691 (+/-0.307) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 1.4454397707459268e-05}
0.714 (+/-0.349) for {'C': 1270574.1052085415, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.693 (+/-0.304) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.692 (+/-0.304) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.692 (+/-0.303) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.692 (+/-0.304) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 8.317637711026698e-06}
0.693 (+/-0.305) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.717 (+/-0.351) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.691 (+/-0.301) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 1.0964781961431852e-05}
0.692 (+/-0.303) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 1.2022644346174139e-05}
0.716 (+/-0.350) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 1.3182567385564057e-05}
0.691 (+/-0.307) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 1.4454397707459268e-05}
0.714 (+/-0.349) for {'C': 1294195.8414499855, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.693 (+/-0.304) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.692 (+/-0.304) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.692 (+/-0.302) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.692 (+/-0.304) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 8.317637711026698e-06}
0.693 (+/-0.305) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.717 (+/-0.351) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.691 (+/-0.301) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.0964781961431852e-05}
0.692 (+/-0.303) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.2022644346174139e-05}
0.716 (+/-0.350) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.3182567385564057e-05}
0.690 (+/-0.306) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.4454397707459268e-05}
0.714 (+/-0.349) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.700 (+/-0.309) for {'C': 0.1, 'kernel': 'linear'}
0.698 (+/-0.306) for {'C': 1.0, 'kernel': 'linear'}
0.694 (+/-0.301) for {'C': 10.0, 'kernel': 'linear'}
0.657 (+/-0.313) for {'C': 100.0, 'kernel': 'linear'}
0.659 (+/-0.314) for {'C': 1000.0, 'kernel': 'linear'}
0.634 (+/-0.324) for {'C': 10000.0, 'kernel': 'linear'}
0.633 (+/-0.324) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.04 0.17 0.06 6
1 0.99 0.96 0.98 623
avg / total 0.98 0.95 0.97 629
本轮grid search结果,得到最好的参数选择是: {'C': 1202264.4346174113, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.717144344133894
第1轮loop中,tunemodel函数得到的最优参数是: ([{'C': array([1180320.63565173, 1184677.11758149, 1189049.6789851 ,
1193438.37921079, 1197843.27782586, 1202264.43461741,
1206701.90959327, 1211155.76298273, 1215626.05523738,
1220112.84703193, 1224616.19926505]), 'kernel': ['rbf'], 'gamma': array([9.12010839e-06, 9.28966387e-06, 9.46237161e-06, 9.63829024e-06,
9.81747943e-06, 1.00000000e-05, 1.01859139e-05, 1.03752842e-05,
1.05681751e-05, 1.07646521e-05, 1.09647820e-05])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}], {'C': 1202264.4346174113, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}, 0.717144344133894)
这是第1次迭代微调C和gamma。
第1次迭代,得到delta: [2.79396772e-09 0.00000000e+00]
SVC模型clf_op_iter的参数是: {'kernel': 'rbf', 'decision_function_shape': 'ovr', 'class_weight': {-1: 15}, 'tol': 0.001, 'gamma': 9.999999999999996e-06, 'random_state': 0, 'cache_size': 200, 'verbose': False, 'degree': 3, 'C': 1202264.4346174113, 'probability': False, 'shrinking': True, 'coef0': 0.0, 'max_iter': -1}
训练模型SVC对训练样本的预测准确率: 0.9447200566973778
测试集中,预测为舞弊样本的有: (array([ 1, 2, 3, 4, 7, 8, 10, 11, 12, 13, 14,
17, 19, 20, 21, 22, 23, 24, 25, 27, 28, 29,
30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40,
41, 42, 43, 44, 45, 46, 47, 49, 50, 51, 52,
53, 54, 57, 58, 59, 60, 61, 62, 63, 64, 65,
66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76,
77, 78, 79, 80, 81, 82, 83, 84, 87, 88, 89,
90, 91, 92, 93, 102, 107, 108, 109, 110, 111, 112,
113, 114, 115, 116, 117, 118, 119, 120, 121, 127, 128,
129, 133, 135, 136, 140, 141, 142, 143, 144, 145, 146,
147, 148, 149, 150, 151, 153, 154, 155, 156, 157, 158,
162, 163, 164, 165, 166, 176, 177, 178, 179, 180, 181,
182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192,
193, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205,
206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216,
217, 218, 219, 223, 225, 226, 227, 228, 229, 230, 231,
232, 233, 234, 235, 236, 237, 238, 239, 242, 243, 244,
245, 246, 247, 251, 252, 253, 254, 255, 256, 257, 258,
259, 260, 261, 262, 264, 267, 268, 269, 270, 274, 275,
276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286,
287, 288, 289, 290, 292, 293, 295, 296, 297, 298, 299,
300, 301, 302, 306, 307, 308, 309, 310, 313, 316, 319,
320, 321, 322, 323, 324, 325, 330, 331, 332, 333, 334,
335, 336, 337, 338, 339, 340, 342, 343, 344, 346, 347,
348, 349, 351, 352, 353, 354, 356, 357, 358, 359, 360,
361, 362, 363, 364, 365, 366, 367, 368, 369, 370, 371,
372, 373, 374, 376, 377, 378, 381, 382, 383, 384, 385,
386, 387, 388, 391, 392, 393, 394, 395, 398, 399, 403,
404, 406, 409, 410, 411, 412, 413, 414, 417, 419, 420,
421, 422, 423, 426, 427, 429, 430, 431, 432, 433, 434,
436, 437, 438, 439, 440, 441, 442, 443, 444, 445, 446,
447, 448, 449, 450, 451, 453, 454, 455, 456, 457, 460,
461, 462, 463, 464, 465, 466, 467, 468, 469, 471, 472,
473, 477, 478, 479, 480, 481, 482, 483, 484, 485, 486,
487, 488, 489, 490, 491, 492, 493, 495, 496, 498, 499,
500, 501, 502, 503, 504, 505, 506, 507, 508, 509, 510,
511, 512, 513, 514, 515, 516, 517, 518, 519, 521, 523,
525, 529, 530, 531, 532, 534, 535, 537, 539, 540, 541,
542, 543, 544, 545, 546, 547, 549, 550, 551, 552, 555,
557, 558, 559, 560, 561, 562, 563, 564, 565, 566, 567,
568, 570, 571, 572, 573, 578, 582, 583, 584, 586, 587,
589, 590, 591, 592, 593, 594, 595, 599, 600, 601, 602,
603, 604, 608, 610, 611, 612, 613, 614, 615, 616, 617,
618, 619, 620, 621, 622, 623, 624, 627, 628, 629, 630,
631, 633, 634, 635, 641, 642, 643, 645, 646, 649, 651,
652, 653, 654, 655, 656, 657, 658, 659, 660, 661, 662,
663, 664, 665, 666, 667, 668, 669, 670, 673, 674, 675,
676, 677, 678, 680, 681, 682, 683, 684, 685, 686, 687,
688, 692, 694, 695, 696, 697, 698, 699, 701, 702, 703,
706, 707, 708, 709, 710, 711, 712, 713, 714, 715, 716,
717, 718, 719, 720, 721, 722, 723, 724, 725, 727, 729,
733, 734, 735, 736, 737, 739, 740, 741, 742, 743, 750,
751, 752, 753, 754, 755, 756, 757, 758, 759, 760, 761,
767, 768, 769, 770, 773, 774, 775, 776, 777, 778, 784,
785, 786, 787, 788, 789, 790, 792, 793, 794, 795, 796,
797, 803, 804, 805, 806, 807, 808, 809, 810, 811, 812,
813, 814, 815, 816, 817, 818, 819, 820, 822, 823, 824,
825, 826, 827, 828, 829, 830, 831, 835, 839, 840, 841,
843, 844, 845, 846, 847, 848, 849, 850, 851, 852, 853,
854, 855, 856, 857, 858, 859, 860, 861, 862, 864, 865,
866, 868, 872, 873, 874, 875, 876, 877, 878, 879, 880,
881, 882, 883, 884, 885, 886, 887, 888, 889, 890, 892,
893, 897, 898, 900, 901, 902, 903, 904, 905, 906, 908,
910, 911, 912, 913, 914, 917, 919, 920, 922, 923, 924,
925, 926, 927, 928, 929, 930, 931, 932, 933, 934, 935,
941, 945, 946, 947, 948, 949, 950, 951, 952, 953, 954,
955, 956, 957, 958, 959, 960, 961, 962, 963, 964, 965,
966, 967, 970, 971, 972, 976, 977, 978, 979, 980, 981,
982, 983, 984, 985, 992, 993, 994, 995, 998, 999, 1000,
1001, 1002, 1003, 1004, 1005, 1006, 1007, 1008, 1009, 1010, 1011,
1012, 1013, 1014, 1015, 1016, 1017, 1020, 1021, 1022, 1027, 1028,
1030, 1034, 1035, 1036, 1037, 1039, 1040, 1041, 1042, 1046, 1047,
1048, 1049, 1050, 1051, 1055, 1056, 1057, 1058, 1060, 1061, 1064,
1065, 1066, 1067, 1068, 1071, 1073, 1074, 1075, 1076, 1079, 1080,
1081, 1082, 1083, 1084, 1085, 1086, 1089, 1090, 1093, 1094, 1095,
1096, 1097, 1098, 1099, 1100, 1101, 1102, 1103, 1104, 1105, 1106,
1107, 1110, 1111, 1112, 1113, 1114, 1115, 1117, 1118, 1119, 1121,
1122, 1123, 1124, 1125, 1127, 1129, 1131, 1132, 1134, 1135, 1137,
1138, 1139, 1140, 1141, 1142, 1144, 1148, 1151, 1152, 1153, 1154,
1156, 1158, 1160, 1162, 1163, 1164, 1167, 1168, 1169, 1170, 1171,
1172, 1175, 1177, 1180, 1183, 1184, 1186, 1188, 1189, 1191, 1192,
1194, 1196, 1199, 1200, 1204, 1205, 1207, 1208, 1210, 1211, 1214,
1215, 1216, 1217, 1218, 1219, 1220, 1222, 1226, 1227, 1228, 1230,
1233, 1234, 1235, 1236, 1237, 1238, 1239, 1240, 1241, 1242, 1243,
1244, 1245, 1246, 1247, 1248, 1249, 1250, 1251, 1252, 1254, 1255,
1256], dtype=int64),)
测试集中,实际为舞弊样本的有: (array([1246, 1247, 1248, 1249, 1250, 1251, 1252, 1253, 1254, 1255, 1256],
dtype=int64),)
预测的舞弊样本数目: 936
训练模型SVC对测试样本的预测准确率: 0.3330970942593905
以上是第42次特征筛选。
第42次特征筛选,AUC值是: 0.5829563694732233
X_train_iter_svc.shape is: (1257, 10)
X_test_iter_svc.shape is: (1257, 10)
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.722 (+/-0.473) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.602 (+/-0.306) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.660 (+/-0.219) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.612 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.698 (+/-0.306) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.20 0.17 0.18 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.99 0.99 629
本轮grid search结果,得到最好的参数选择是: {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6982356336054085
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.660 (+/-0.219) for {'C': 1.0, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.577 (+/-0.186) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.698 (+/-0.306) for {'C': 1.0, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.658 (+/-0.312) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6982356336054085
RBF的粗grid search最佳超参: {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001} RBF的粗grid search最高分值: 0.6982356336054085
linear的粗grid search最佳超参: {'C': 1.0, 'kernel': 'linear'} linear的粗grid search最高分值: 0.6982356336054085
粗grid search得到的parameter是:
[{'C': array([1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02, 1.e+03, 1.e+04, 1.e+05,
1.e+06, 1.e+07, 1.e+08]), 'kernel': ['rbf'], 'gamma': array([1.e-08, 1.e-07, 1.e-06, 1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01,
1.e+00, 1.e+01, 1.e+02])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.848 (+/-0.459) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.848 (+/-0.459) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.690 (+/-0.300) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.611 (+/-0.308) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.848 (+/-0.459) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.660 (+/-0.219) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.604 (+/-0.159) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.601 (+/-0.307) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.848 (+/-0.459) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.660 (+/-0.219) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.594 (+/-0.157) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.567 (+/-0.197) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.602 (+/-0.306) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.848 (+/-0.459) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.660 (+/-0.219) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.594 (+/-0.157) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.589 (+/-0.188) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.574 (+/-0.192) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.602 (+/-0.306) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.848 (+/-0.459) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.660 (+/-0.219) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.594 (+/-0.157) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.589 (+/-0.187) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.565 (+/-0.196) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.574 (+/-0.191) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.602 (+/-0.306) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.765 (+/-0.428) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.660 (+/-0.219) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.595 (+/-0.154) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.591 (+/-0.191) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.590 (+/-0.193) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.572 (+/-0.191) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.574 (+/-0.191) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.602 (+/-0.306) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.848 (+/-0.460) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.691 (+/-0.371) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.616 (+/-0.182) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.587 (+/-0.186) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.581 (+/-0.187) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.581 (+/-0.199) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.573 (+/-0.191) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.574 (+/-0.191) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.602 (+/-0.306) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.793 (+/-0.440) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.642 (+/-0.370) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.600 (+/-0.181) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.593 (+/-0.192) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.581 (+/-0.187) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.586 (+/-0.192) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.573 (+/-0.191) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.574 (+/-0.191) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.602 (+/-0.306) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.690 (+/-0.300) for {'C': 1.0, 'kernel': 'linear'}
0.599 (+/-0.162) for {'C': 10.0, 'kernel': 'linear'}
0.599 (+/-0.192) for {'C': 100.0, 'kernel': 'linear'}
0.580 (+/-0.187) for {'C': 1000.0, 'kernel': 'linear'}
0.590 (+/-0.191) for {'C': 10000.0, 'kernel': 'linear'}
0.588 (+/-0.193) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [1]
检查交叉验证集中测试集标签是否有预测不到的值: {-1}
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 1.00 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.658 (+/-0.217) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.658 (+/-0.217) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.699 (+/-0.307) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.236) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.007) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.658 (+/-0.217) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.306) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.696 (+/-0.303) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.611 (+/-0.240) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.497 (+/-0.006) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.658 (+/-0.217) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.698 (+/-0.306) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.695 (+/-0.303) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.587 (+/-0.231) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.612 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.497 (+/-0.006) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.658 (+/-0.217) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.698 (+/-0.306) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.695 (+/-0.303) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.661 (+/-0.314) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.611 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.497 (+/-0.006) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.658 (+/-0.217) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.698 (+/-0.306) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.695 (+/-0.303) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.661 (+/-0.314) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.584 (+/-0.234) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.497 (+/-0.006) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.699 (+/-0.308) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.698 (+/-0.306) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.696 (+/-0.303) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.661 (+/-0.315) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.660 (+/-0.314) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.611 (+/-0.238) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.237) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.237) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.497 (+/-0.006) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.683 (+/-0.296) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.686 (+/-0.265) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.697 (+/-0.305) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.661 (+/-0.314) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.658 (+/-0.311) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.634 (+/-0.323) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.611 (+/-0.238) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.237) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.237) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.497 (+/-0.006) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.699 (+/-0.307) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.660 (+/-0.231) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.695 (+/-0.303) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.661 (+/-0.315) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.658 (+/-0.312) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.659 (+/-0.311) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.611 (+/-0.237) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.237) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.237) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.497 (+/-0.006) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.699 (+/-0.307) for {'C': 1.0, 'kernel': 'linear'}
0.696 (+/-0.303) for {'C': 10.0, 'kernel': 'linear'}
0.663 (+/-0.314) for {'C': 100.0, 'kernel': 'linear'}
0.659 (+/-0.313) for {'C': 1000.0, 'kernel': 'linear'}
0.660 (+/-0.314) for {'C': 10000.0, 'kernel': 'linear'}
0.659 (+/-0.313) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6991982026547944
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.673 (+/-0.294) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.667 (+/-0.292) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.631 (+/-0.319) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.667 (+/-0.292) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.199) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.592 (+/-0.295) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.667 (+/-0.292) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.628 (+/-0.188) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.589 (+/-0.154) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.601 (+/-0.307) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.667 (+/-0.292) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.628 (+/-0.188) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.579 (+/-0.146) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.571 (+/-0.193) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.602 (+/-0.306) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.642 (+/-0.204) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.628 (+/-0.188) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.579 (+/-0.146) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.577 (+/-0.187) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.574 (+/-0.192) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.602 (+/-0.306) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.747 (+/-0.502) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.642 (+/-0.204) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.626 (+/-0.188) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.579 (+/-0.146) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.575 (+/-0.187) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.571 (+/-0.191) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.574 (+/-0.191) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.602 (+/-0.306) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.547 (+/-0.301) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.681 (+/-0.307) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.618 (+/-0.196) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.582 (+/-0.146) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.575 (+/-0.186) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.587 (+/-0.193) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.572 (+/-0.191) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.574 (+/-0.191) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.602 (+/-0.306) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.831 (+/-0.449) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.538 (+/-0.143) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.570 (+/-0.145) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.577 (+/-0.185) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.582 (+/-0.187) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.581 (+/-0.199) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.573 (+/-0.191) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.574 (+/-0.191) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.602 (+/-0.306) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.793 (+/-0.440) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.552 (+/-0.297) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.581 (+/-0.163) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.585 (+/-0.193) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.581 (+/-0.187) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.586 (+/-0.192) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.573 (+/-0.191) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.574 (+/-0.191) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.602 (+/-0.306) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.798 (+/-0.437) for {'C': 0.1, 'kernel': 'linear'}
0.660 (+/-0.219) for {'C': 1.0, 'kernel': 'linear'}
0.585 (+/-0.147) for {'C': 10.0, 'kernel': 'linear'}
0.579 (+/-0.189) for {'C': 100.0, 'kernel': 'linear'}
0.577 (+/-0.186) for {'C': 1000.0, 'kernel': 'linear'}
0.588 (+/-0.191) for {'C': 10000.0, 'kernel': 'linear'}
0.587 (+/-0.193) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.682 (+/-0.294) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.698 (+/-0.306) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.494 (+/-0.012) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.306) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.305) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.611 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.007) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.698 (+/-0.306) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.304) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.693 (+/-0.303) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.610 (+/-0.240) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.497 (+/-0.006) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.698 (+/-0.306) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.304) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.693 (+/-0.304) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.607 (+/-0.241) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.612 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.497 (+/-0.006) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.698 (+/-0.306) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.304) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.693 (+/-0.304) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.656 (+/-0.311) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.611 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.497 (+/-0.006) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.617 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.698 (+/-0.306) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.697 (+/-0.304) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.692 (+/-0.304) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.656 (+/-0.311) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.608 (+/-0.242) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.497 (+/-0.006) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.525 (+/-0.150) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.698 (+/-0.309) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.696 (+/-0.301) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.693 (+/-0.305) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.657 (+/-0.310) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.659 (+/-0.313) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.611 (+/-0.238) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.237) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.237) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.497 (+/-0.006) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.700 (+/-0.309) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.652 (+/-0.227) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.692 (+/-0.299) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.680 (+/-0.365) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.658 (+/-0.312) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.634 (+/-0.323) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.611 (+/-0.238) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.237) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.237) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.497 (+/-0.006) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.699 (+/-0.307) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.575 (+/-0.206) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.693 (+/-0.303) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.680 (+/-0.366) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.658 (+/-0.312) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.659 (+/-0.311) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.611 (+/-0.237) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.237) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.237) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.497 (+/-0.006) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.683 (+/-0.296) for {'C': 0.1, 'kernel': 'linear'}
0.698 (+/-0.306) for {'C': 1.0, 'kernel': 'linear'}
0.694 (+/-0.301) for {'C': 10.0, 'kernel': 'linear'}
0.656 (+/-0.311) for {'C': 100.0, 'kernel': 'linear'}
0.658 (+/-0.312) for {'C': 1000.0, 'kernel': 'linear'}
0.659 (+/-0.313) for {'C': 10000.0, 'kernel': 'linear'}
0.659 (+/-0.313) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6998392282958199
发现最优参数gamma为原先的最大/最小值,直接重新设置超参。
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-09}
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-08}
0.765 (+/-0.428) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-07}
0.660 (+/-0.219) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-06}
0.595 (+/-0.154) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-05}
0.591 (+/-0.191) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 0.0001}
0.590 (+/-0.193) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-09}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-08}
0.773 (+/-0.417) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-07}
0.669 (+/-0.295) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-06}
0.596 (+/-0.159) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-05}
0.584 (+/-0.187) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 0.0001}
0.589 (+/-0.194) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-09}
0.747 (+/-0.502) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-08}
0.735 (+/-0.392) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-07}
0.630 (+/-0.189) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-06}
0.598 (+/-0.159) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-05}
0.583 (+/-0.186) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 0.0001}
0.588 (+/-0.193) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-09}
0.747 (+/-0.502) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-08}
0.798 (+/-0.438) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-07}
0.630 (+/-0.189) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-06}
0.594 (+/-0.191) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-05}
0.579 (+/-0.186) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 0.0001}
0.586 (+/-0.193) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-09}
0.797 (+/-0.492) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-08}
0.726 (+/-0.399) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-07}
0.630 (+/-0.189) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-06}
0.593 (+/-0.188) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-05}
0.577 (+/-0.185) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 0.0001}
0.585 (+/-0.193) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-09}
0.848 (+/-0.460) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-08}
0.690 (+/-0.372) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-07}
0.616 (+/-0.182) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-06}
0.587 (+/-0.186) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-05}
0.581 (+/-0.187) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 0.0001}
0.581 (+/-0.199) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-09}
0.823 (+/-0.451) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-08}
0.686 (+/-0.374) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-07}
0.602 (+/-0.180) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-06}
0.590 (+/-0.187) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-05}
0.582 (+/-0.187) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 0.0001}
0.580 (+/-0.199) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-10}
0.747 (+/-0.502) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-09}
0.815 (+/-0.434) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-08}
0.677 (+/-0.372) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-07}
0.600 (+/-0.181) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-06}
0.590 (+/-0.187) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-05}
0.581 (+/-0.188) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.0001}
0.580 (+/-0.199) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-10}
0.747 (+/-0.502) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-09}
0.806 (+/-0.436) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-08}
0.656 (+/-0.367) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-07}
0.600 (+/-0.181) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-06}
0.594 (+/-0.192) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-05}
0.581 (+/-0.188) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 0.0001}
0.580 (+/-0.199) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-10}
0.797 (+/-0.492) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-09}
0.798 (+/-0.437) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-08}
0.646 (+/-0.370) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-07}
0.600 (+/-0.181) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-06}
0.593 (+/-0.192) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-05}
0.581 (+/-0.188) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 0.0001}
0.585 (+/-0.193) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-10}
0.848 (+/-0.460) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-09}
0.793 (+/-0.440) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-08}
0.642 (+/-0.370) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-07}
0.600 (+/-0.181) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-06}
0.593 (+/-0.192) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-05}
0.581 (+/-0.187) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 0.0001}
0.586 (+/-0.192) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.690 (+/-0.300) for {'C': 1.0, 'kernel': 'linear'}
0.599 (+/-0.162) for {'C': 10.0, 'kernel': 'linear'}
0.599 (+/-0.192) for {'C': 100.0, 'kernel': 'linear'}
0.580 (+/-0.187) for {'C': 1000.0, 'kernel': 'linear'}
0.590 (+/-0.191) for {'C': 10000.0, 'kernel': 'linear'}
0.588 (+/-0.193) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [1]
检查交叉验证集中测试集标签是否有预测不到的值: {-1}
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 1.00 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-09}
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-08}
0.699 (+/-0.308) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-07}
0.698 (+/-0.306) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-06}
0.696 (+/-0.303) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-05}
0.661 (+/-0.315) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 0.0001}
0.660 (+/-0.314) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-09}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-08}
0.699 (+/-0.309) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-07}
0.698 (+/-0.305) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-06}
0.695 (+/-0.302) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-05}
0.660 (+/-0.315) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 0.0001}
0.659 (+/-0.315) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-09}
0.617 (+/-0.238) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-08}
0.699 (+/-0.307) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-07}
0.697 (+/-0.305) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-06}
0.679 (+/-0.291) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-05}
0.660 (+/-0.314) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 0.0001}
0.659 (+/-0.314) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-09}
0.617 (+/-0.238) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-08}
0.700 (+/-0.309) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-07}
0.697 (+/-0.305) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-06}
0.662 (+/-0.312) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-05}
0.659 (+/-0.314) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 0.0001}
0.659 (+/-0.313) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-09}
0.642 (+/-0.236) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-08}
0.682 (+/-0.291) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-07}
0.697 (+/-0.305) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-06}
0.662 (+/-0.314) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-05}
0.658 (+/-0.311) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 0.0001}
0.658 (+/-0.311) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-09}
0.683 (+/-0.296) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-08}
0.677 (+/-0.244) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-07}
0.697 (+/-0.305) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-06}
0.661 (+/-0.314) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-05}
0.658 (+/-0.311) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 0.0001}
0.634 (+/-0.323) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-09}
0.683 (+/-0.296) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-08}
0.655 (+/-0.211) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-07}
0.696 (+/-0.303) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-06}
0.661 (+/-0.315) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-05}
0.658 (+/-0.312) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 0.0001}
0.633 (+/-0.322) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-10}
0.617 (+/-0.238) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-09}
0.700 (+/-0.308) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-08}
0.653 (+/-0.209) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-07}
0.695 (+/-0.303) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-06}
0.661 (+/-0.315) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-05}
0.658 (+/-0.312) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.0001}
0.633 (+/-0.322) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-10}
0.617 (+/-0.238) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-09}
0.700 (+/-0.308) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-08}
0.664 (+/-0.232) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-07}
0.695 (+/-0.303) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-06}
0.661 (+/-0.315) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-05}
0.658 (+/-0.311) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 0.0001}
0.634 (+/-0.321) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-10}
0.642 (+/-0.236) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-09}
0.699 (+/-0.307) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-08}
0.662 (+/-0.231) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-07}
0.695 (+/-0.303) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-06}
0.661 (+/-0.315) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-05}
0.658 (+/-0.311) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 0.0001}
0.659 (+/-0.310) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-10}
0.683 (+/-0.296) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-09}
0.699 (+/-0.307) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-08}
0.658 (+/-0.232) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-07}
0.695 (+/-0.303) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-06}
0.661 (+/-0.315) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-05}
0.658 (+/-0.312) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 0.0001}
0.659 (+/-0.311) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.699 (+/-0.307) for {'C': 1.0, 'kernel': 'linear'}
0.696 (+/-0.303) for {'C': 10.0, 'kernel': 'linear'}
0.663 (+/-0.314) for {'C': 100.0, 'kernel': 'linear'}
0.659 (+/-0.313) for {'C': 1000.0, 'kernel': 'linear'}
0.660 (+/-0.314) for {'C': 10000.0, 'kernel': 'linear'}
0.659 (+/-0.313) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-07}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6996789718855635
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-09}
0.547 (+/-0.301) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-08}
0.681 (+/-0.307) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-07}
0.618 (+/-0.196) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-06}
0.582 (+/-0.146) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-05}
0.575 (+/-0.186) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 0.0001}
0.587 (+/-0.193) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-09}
0.747 (+/-0.502) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-08}
0.666 (+/-0.298) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-07}
0.616 (+/-0.291) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-06}
0.583 (+/-0.154) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-05}
0.575 (+/-0.186) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 0.0001}
0.586 (+/-0.193) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-09}
0.747 (+/-0.502) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-08}
0.658 (+/-0.292) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-07}
0.582 (+/-0.178) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-06}
0.584 (+/-0.153) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-05}
0.576 (+/-0.186) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 0.0001}
0.586 (+/-0.193) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-09}
0.797 (+/-0.492) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-08}
0.623 (+/-0.293) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-07}
0.566 (+/-0.145) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-06}
0.581 (+/-0.180) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-05}
0.576 (+/-0.185) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 0.0001}
0.586 (+/-0.193) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-09}
0.848 (+/-0.460) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-08}
0.556 (+/-0.140) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-07}
0.568 (+/-0.146) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-06}
0.577 (+/-0.185) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-05}
0.577 (+/-0.185) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 0.0001}
0.585 (+/-0.193) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-10}
0.547 (+/-0.301) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-09}
0.831 (+/-0.449) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-08}
0.541 (+/-0.143) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-07}
0.570 (+/-0.145) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-06}
0.577 (+/-0.185) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-05}
0.582 (+/-0.187) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 0.0001}
0.581 (+/-0.199) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-10}
0.747 (+/-0.502) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-09}
0.815 (+/-0.434) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-08}
0.534 (+/-0.144) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-07}
0.569 (+/-0.146) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-06}
0.581 (+/-0.187) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-05}
0.581 (+/-0.187) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 0.0001}
0.580 (+/-0.199) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-10}
0.747 (+/-0.502) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-09}
0.806 (+/-0.436) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-08}
0.529 (+/-0.146) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-07}
0.577 (+/-0.158) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-06}
0.581 (+/-0.187) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-05}
0.581 (+/-0.188) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.0001}
0.580 (+/-0.199) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-10}
0.797 (+/-0.492) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-09}
0.798 (+/-0.437) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-08}
0.553 (+/-0.296) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-07}
0.578 (+/-0.156) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-06}
0.585 (+/-0.193) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-05}
0.581 (+/-0.188) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 0.0001}
0.580 (+/-0.199) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-10}
0.848 (+/-0.460) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-09}
0.798 (+/-0.437) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-08}
0.552 (+/-0.297) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-07}
0.576 (+/-0.154) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-06}
0.585 (+/-0.193) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-05}
0.581 (+/-0.188) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 0.0001}
0.585 (+/-0.193) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-11}
0.547 (+/-0.301) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-10}
0.831 (+/-0.449) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-09}
0.793 (+/-0.440) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-08}
0.552 (+/-0.297) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-07}
0.581 (+/-0.163) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-06}
0.585 (+/-0.193) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-05}
0.581 (+/-0.187) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 0.0001}
0.586 (+/-0.192) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.798 (+/-0.437) for {'C': 0.1, 'kernel': 'linear'}
0.660 (+/-0.219) for {'C': 1.0, 'kernel': 'linear'}
0.585 (+/-0.147) for {'C': 10.0, 'kernel': 'linear'}
0.579 (+/-0.189) for {'C': 100.0, 'kernel': 'linear'}
0.577 (+/-0.186) for {'C': 1000.0, 'kernel': 'linear'}
0.588 (+/-0.191) for {'C': 10000.0, 'kernel': 'linear'}
0.587 (+/-0.193) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-09}
0.525 (+/-0.150) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-08}
0.698 (+/-0.309) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-07}
0.696 (+/-0.301) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-06}
0.693 (+/-0.305) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1e-05}
0.657 (+/-0.310) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 0.0001}
0.659 (+/-0.313) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-09}
0.617 (+/-0.238) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-08}
0.698 (+/-0.308) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-07}
0.694 (+/-0.297) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-06}
0.692 (+/-0.306) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1e-05}
0.656 (+/-0.311) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 0.0001}
0.658 (+/-0.314) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-09}
0.617 (+/-0.238) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-08}
0.698 (+/-0.307) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-07}
0.692 (+/-0.295) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-06}
0.699 (+/-0.345) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1e-05}
0.657 (+/-0.311) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 0.0001}
0.659 (+/-0.313) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-09}
0.642 (+/-0.236) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-08}
0.695 (+/-0.300) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-07}
0.691 (+/-0.295) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-06}
0.706 (+/-0.338) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-05}
0.657 (+/-0.311) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 0.0001}
0.659 (+/-0.313) for {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-09}
0.683 (+/-0.296) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-08}
0.683 (+/-0.276) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-07}
0.691 (+/-0.298) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-06}
0.681 (+/-0.364) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1e-05}
0.658 (+/-0.312) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 0.0001}
0.658 (+/-0.311) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-10}
0.525 (+/-0.150) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-09}
0.700 (+/-0.309) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-08}
0.658 (+/-0.238) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-07}
0.692 (+/-0.299) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-06}
0.680 (+/-0.365) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1e-05}
0.658 (+/-0.312) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 0.0001}
0.634 (+/-0.323) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-10}
0.617 (+/-0.238) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-09}
0.700 (+/-0.308) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-08}
0.632 (+/-0.212) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-07}
0.692 (+/-0.298) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-06}
0.680 (+/-0.365) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 1e-05}
0.658 (+/-0.312) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 0.0001}
0.633 (+/-0.322) for {'C': 15848931.924611133, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-10}
0.617 (+/-0.238) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-09}
0.700 (+/-0.308) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-08}
0.603 (+/-0.195) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-07}
0.692 (+/-0.302) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-06}
0.680 (+/-0.366) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 1e-05}
0.658 (+/-0.312) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.0001}
0.633 (+/-0.322) for {'C': 25118864.31509581, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-10}
0.642 (+/-0.236) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-09}
0.699 (+/-0.307) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-08}
0.588 (+/-0.210) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-07}
0.693 (+/-0.302) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-06}
0.680 (+/-0.367) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 1e-05}
0.658 (+/-0.311) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 0.0001}
0.634 (+/-0.321) for {'C': 39810717.05534975, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-10}
0.683 (+/-0.296) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-09}
0.699 (+/-0.307) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-08}
0.585 (+/-0.209) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-07}
0.693 (+/-0.302) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-06}
0.679 (+/-0.366) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 1e-05}
0.658 (+/-0.311) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 0.0001}
0.659 (+/-0.310) for {'C': 63095734.448019244, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-11}
0.525 (+/-0.150) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-10}
0.700 (+/-0.309) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-09}
0.699 (+/-0.307) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-08}
0.578 (+/-0.209) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-07}
0.693 (+/-0.303) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-06}
0.680 (+/-0.366) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 1e-05}
0.658 (+/-0.312) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 0.0001}
0.659 (+/-0.311) for {'C': 99999999.99999991, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.683 (+/-0.296) for {'C': 0.1, 'kernel': 'linear'}
0.698 (+/-0.306) for {'C': 1.0, 'kernel': 'linear'}
0.694 (+/-0.301) for {'C': 10.0, 'kernel': 'linear'}
0.656 (+/-0.311) for {'C': 100.0, 'kernel': 'linear'}
0.658 (+/-0.312) for {'C': 1000.0, 'kernel': 'linear'}
0.659 (+/-0.313) for {'C': 10000.0, 'kernel': 'linear'}
0.659 (+/-0.313) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.07 0.17 0.10 6
1 0.99 0.98 0.98 623
avg / total 0.98 0.97 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 3981071.705534967, 'kernel': 'rbf', 'gamma': 1e-05}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.7060876824140491
循环迭代之前,delta is: [6.01892829e+06 9.99000000e-06]
执行tunemodel()函数前,使用的grid search parameter是:
[{'C': array([2511886.43150958, 2754228.70333817, 3019951.72040201,
3311311.21482591, 3630780.54770102, 3981071.70553498,
4365158.32240166, 4786300.92322638, 5248074.60249773,
5754399.37337156, 6309573.44480193]), 'kernel': ['rbf'], 'gamma': array([1.00000000e-06, 1.58489319e-06, 2.51188643e-06, 3.98107171e-06,
6.30957344e-06, 1.00000000e-05, 1.58489319e-05, 2.51188643e-05,
3.98107171e-05, 6.30957344e-05, 1.00000000e-04])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.630 (+/-0.189) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.666 (+/-0.298) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.597 (+/-0.161) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.604 (+/-0.180) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.597 (+/-0.156) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.598 (+/-0.159) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.616 (+/-0.236) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.588 (+/-0.185) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.588 (+/-0.185) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.584 (+/-0.190) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.583 (+/-0.186) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.630 (+/-0.189) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.666 (+/-0.298) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.595 (+/-0.160) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.591 (+/-0.153) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.599 (+/-0.157) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.585 (+/-0.169) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.616 (+/-0.236) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.588 (+/-0.185) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.584 (+/-0.184) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.584 (+/-0.190) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.583 (+/-0.186) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.630 (+/-0.189) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.665 (+/-0.299) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.594 (+/-0.159) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.592 (+/-0.154) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.594 (+/-0.153) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.593 (+/-0.190) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.616 (+/-0.236) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.588 (+/-0.185) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.583 (+/-0.185) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.584 (+/-0.190) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.582 (+/-0.186) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.630 (+/-0.189) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.660 (+/-0.295) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.594 (+/-0.159) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.592 (+/-0.154) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.603 (+/-0.175) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.594 (+/-0.191) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.618 (+/-0.236) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.588 (+/-0.185) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.583 (+/-0.185) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.584 (+/-0.190) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.579 (+/-0.186) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.630 (+/-0.189) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.655 (+/-0.295) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.593 (+/-0.159) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.594 (+/-0.154) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.602 (+/-0.175) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.597 (+/-0.193) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.605 (+/-0.198) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.585 (+/-0.184) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.581 (+/-0.185) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.584 (+/-0.190) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.579 (+/-0.186) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.630 (+/-0.189) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.644 (+/-0.288) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.590 (+/-0.155) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.592 (+/-0.152) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.606 (+/-0.179) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.594 (+/-0.191) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.598 (+/-0.192) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.585 (+/-0.184) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.582 (+/-0.185) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.585 (+/-0.190) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.579 (+/-0.186) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.630 (+/-0.189) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.643 (+/-0.289) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.590 (+/-0.155) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.593 (+/-0.152) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.596 (+/-0.191) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.594 (+/-0.191) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.599 (+/-0.192) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.585 (+/-0.184) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.581 (+/-0.185) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.585 (+/-0.190) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.578 (+/-0.185) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.630 (+/-0.189) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.635 (+/-0.288) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.589 (+/-0.153) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.595 (+/-0.153) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.596 (+/-0.191) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.595 (+/-0.191) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.599 (+/-0.192) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.583 (+/-0.184) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.582 (+/-0.185) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.588 (+/-0.194) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.577 (+/-0.186) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.630 (+/-0.189) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.630 (+/-0.288) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.594 (+/-0.159) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.593 (+/-0.153) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.597 (+/-0.191) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.595 (+/-0.191) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.599 (+/-0.192) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.583 (+/-0.184) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.572 (+/-0.161) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.588 (+/-0.194) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.578 (+/-0.185) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.630 (+/-0.189) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.629 (+/-0.288) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.590 (+/-0.155) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.599 (+/-0.175) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.597 (+/-0.191) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.597 (+/-0.193) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.595 (+/-0.189) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.582 (+/-0.185) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.571 (+/-0.160) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.588 (+/-0.194) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.577 (+/-0.185) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.630 (+/-0.189) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.627 (+/-0.289) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.593 (+/-0.155) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.598 (+/-0.175) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.594 (+/-0.190) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.593 (+/-0.188) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.595 (+/-0.189) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.582 (+/-0.185) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.571 (+/-0.161) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.588 (+/-0.194) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.577 (+/-0.185) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.690 (+/-0.300) for {'C': 1.0, 'kernel': 'linear'}
0.599 (+/-0.162) for {'C': 10.0, 'kernel': 'linear'}
0.599 (+/-0.192) for {'C': 100.0, 'kernel': 'linear'}
0.580 (+/-0.187) for {'C': 1000.0, 'kernel': 'linear'}
0.590 (+/-0.191) for {'C': 10000.0, 'kernel': 'linear'}
0.588 (+/-0.193) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.697 (+/-0.305) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.698 (+/-0.307) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.696 (+/-0.303) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.695 (+/-0.302) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.695 (+/-0.301) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.679 (+/-0.291) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.664 (+/-0.316) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.661 (+/-0.312) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.661 (+/-0.313) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.659 (+/-0.315) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.660 (+/-0.314) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.697 (+/-0.305) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.698 (+/-0.307) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.695 (+/-0.302) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.695 (+/-0.302) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.695 (+/-0.301) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.662 (+/-0.311) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.663 (+/-0.316) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.661 (+/-0.312) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.660 (+/-0.312) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.659 (+/-0.315) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.660 (+/-0.314) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.697 (+/-0.305) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.698 (+/-0.306) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.695 (+/-0.301) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.695 (+/-0.302) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.679 (+/-0.290) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.662 (+/-0.311) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.663 (+/-0.316) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.661 (+/-0.312) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.660 (+/-0.313) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.659 (+/-0.315) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.659 (+/-0.314) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.697 (+/-0.305) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.698 (+/-0.306) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.695 (+/-0.302) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.695 (+/-0.302) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.679 (+/-0.291) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.662 (+/-0.312) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.663 (+/-0.317) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.661 (+/-0.313) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.660 (+/-0.313) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.659 (+/-0.315) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.659 (+/-0.313) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.697 (+/-0.305) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.697 (+/-0.306) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.695 (+/-0.302) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.695 (+/-0.303) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.679 (+/-0.290) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.662 (+/-0.314) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.663 (+/-0.316) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.660 (+/-0.313) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.660 (+/-0.312) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.659 (+/-0.315) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.659 (+/-0.313) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.697 (+/-0.305) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.697 (+/-0.305) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.695 (+/-0.303) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.695 (+/-0.302) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.679 (+/-0.290) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.662 (+/-0.312) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.662 (+/-0.316) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.660 (+/-0.313) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.660 (+/-0.312) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.659 (+/-0.315) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.659 (+/-0.314) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.697 (+/-0.305) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.696 (+/-0.304) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.695 (+/-0.303) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.695 (+/-0.303) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.662 (+/-0.311) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.662 (+/-0.312) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.662 (+/-0.316) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.661 (+/-0.313) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.660 (+/-0.312) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.659 (+/-0.315) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.659 (+/-0.313) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.697 (+/-0.305) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.696 (+/-0.303) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.695 (+/-0.301) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.695 (+/-0.303) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.662 (+/-0.311) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.662 (+/-0.313) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.662 (+/-0.316) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.660 (+/-0.313) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.660 (+/-0.312) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.659 (+/-0.316) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.658 (+/-0.312) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.697 (+/-0.305) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.696 (+/-0.303) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.695 (+/-0.302) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.695 (+/-0.303) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.662 (+/-0.312) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.662 (+/-0.314) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.662 (+/-0.316) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.660 (+/-0.312) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.659 (+/-0.311) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.659 (+/-0.316) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.659 (+/-0.312) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.697 (+/-0.305) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.696 (+/-0.303) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.695 (+/-0.303) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.695 (+/-0.302) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.662 (+/-0.312) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.662 (+/-0.314) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.662 (+/-0.316) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.660 (+/-0.312) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.659 (+/-0.310) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.659 (+/-0.316) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.658 (+/-0.313) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.697 (+/-0.305) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.696 (+/-0.303) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.695 (+/-0.303) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.695 (+/-0.302) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.662 (+/-0.313) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.662 (+/-0.314) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.661 (+/-0.316) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.660 (+/-0.312) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.659 (+/-0.311) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.659 (+/-0.316) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.658 (+/-0.311) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.699 (+/-0.307) for {'C': 1.0, 'kernel': 'linear'}
0.696 (+/-0.303) for {'C': 10.0, 'kernel': 'linear'}
0.663 (+/-0.314) for {'C': 100.0, 'kernel': 'linear'}
0.659 (+/-0.313) for {'C': 1000.0, 'kernel': 'linear'}
0.660 (+/-0.314) for {'C': 10000.0, 'kernel': 'linear'}
0.659 (+/-0.313) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6985561464259213
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.582 (+/-0.178) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.585 (+/-0.178) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.579 (+/-0.142) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.587 (+/-0.152) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.590 (+/-0.156) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.584 (+/-0.153) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.583 (+/-0.189) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.577 (+/-0.185) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.579 (+/-0.189) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.575 (+/-0.187) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.576 (+/-0.186) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.583 (+/-0.179) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.586 (+/-0.178) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.579 (+/-0.142) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.587 (+/-0.152) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.597 (+/-0.149) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.572 (+/-0.159) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.583 (+/-0.189) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.577 (+/-0.185) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.576 (+/-0.187) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.575 (+/-0.186) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.577 (+/-0.185) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.581 (+/-0.180) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.577 (+/-0.152) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.579 (+/-0.142) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.587 (+/-0.152) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.592 (+/-0.146) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.583 (+/-0.179) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.583 (+/-0.189) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.578 (+/-0.185) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.576 (+/-0.187) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.575 (+/-0.187) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.577 (+/-0.185) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.572 (+/-0.154) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.574 (+/-0.145) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.578 (+/-0.142) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.586 (+/-0.152) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.600 (+/-0.170) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.583 (+/-0.179) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.583 (+/-0.189) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.575 (+/-0.187) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.576 (+/-0.187) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.575 (+/-0.187) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.575 (+/-0.186) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.567 (+/-0.145) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.575 (+/-0.145) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.578 (+/-0.142) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.587 (+/-0.152) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.599 (+/-0.171) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.582 (+/-0.179) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.583 (+/-0.189) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.575 (+/-0.187) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.576 (+/-0.187) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.575 (+/-0.186) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.575 (+/-0.186) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.566 (+/-0.145) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.575 (+/-0.144) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.580 (+/-0.144) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.587 (+/-0.153) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.604 (+/-0.175) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.581 (+/-0.180) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.584 (+/-0.189) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.574 (+/-0.187) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.576 (+/-0.187) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.577 (+/-0.187) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.576 (+/-0.185) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.567 (+/-0.145) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.575 (+/-0.144) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.580 (+/-0.144) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.588 (+/-0.152) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.593 (+/-0.187) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.577 (+/-0.185) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.584 (+/-0.189) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.574 (+/-0.187) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.575 (+/-0.186) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.577 (+/-0.187) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.576 (+/-0.185) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.568 (+/-0.146) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.575 (+/-0.144) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.584 (+/-0.146) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.588 (+/-0.152) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.593 (+/-0.187) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.577 (+/-0.185) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.584 (+/-0.189) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.574 (+/-0.187) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.577 (+/-0.187) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.577 (+/-0.187) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.576 (+/-0.186) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.568 (+/-0.146) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.572 (+/-0.143) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.588 (+/-0.153) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.587 (+/-0.152) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.593 (+/-0.187) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.577 (+/-0.185) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.584 (+/-0.189) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.576 (+/-0.188) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.568 (+/-0.162) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.577 (+/-0.187) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.576 (+/-0.185) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.568 (+/-0.146) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.575 (+/-0.144) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.587 (+/-0.153) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.594 (+/-0.176) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.593 (+/-0.187) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.577 (+/-0.185) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.584 (+/-0.189) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.576 (+/-0.188) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.568 (+/-0.162) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.577 (+/-0.187) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.576 (+/-0.185) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.568 (+/-0.146) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.575 (+/-0.144) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.589 (+/-0.154) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.593 (+/-0.176) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.590 (+/-0.186) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.577 (+/-0.185) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.584 (+/-0.189) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.576 (+/-0.188) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.568 (+/-0.162) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.577 (+/-0.187) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.577 (+/-0.185) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.798 (+/-0.437) for {'C': 0.1, 'kernel': 'linear'}
0.660 (+/-0.219) for {'C': 1.0, 'kernel': 'linear'}
0.585 (+/-0.147) for {'C': 10.0, 'kernel': 'linear'}
0.579 (+/-0.189) for {'C': 100.0, 'kernel': 'linear'}
0.577 (+/-0.186) for {'C': 1000.0, 'kernel': 'linear'}
0.588 (+/-0.191) for {'C': 10000.0, 'kernel': 'linear'}
0.587 (+/-0.193) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.692 (+/-0.295) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.692 (+/-0.297) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.694 (+/-0.302) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.694 (+/-0.303) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.716 (+/-0.351) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.699 (+/-0.345) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.681 (+/-0.363) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.681 (+/-0.364) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.656 (+/-0.311) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.656 (+/-0.311) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.657 (+/-0.311) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.692 (+/-0.297) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.693 (+/-0.298) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.694 (+/-0.302) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.694 (+/-0.303) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.741 (+/-0.317) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.682 (+/-0.367) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.681 (+/-0.363) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.680 (+/-0.364) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.656 (+/-0.311) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.656 (+/-0.312) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.658 (+/-0.311) for {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.692 (+/-0.297) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.693 (+/-0.298) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.694 (+/-0.302) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.694 (+/-0.303) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.724 (+/-0.314) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.707 (+/-0.340) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.681 (+/-0.364) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.681 (+/-0.365) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.656 (+/-0.311) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.656 (+/-0.311) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.658 (+/-0.311) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.691 (+/-0.296) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.693 (+/-0.298) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.693 (+/-0.301) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.694 (+/-0.303) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.724 (+/-0.314) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.707 (+/-0.340) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.680 (+/-0.364) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.656 (+/-0.311) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.656 (+/-0.310) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.656 (+/-0.311) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.657 (+/-0.311) for {'C': 3311311.214825911, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.691 (+/-0.295) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.693 (+/-0.298) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.693 (+/-0.302) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.694 (+/-0.303) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.724 (+/-0.313) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.706 (+/-0.339) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.680 (+/-0.364) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.655 (+/-0.311) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.656 (+/-0.310) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.656 (+/-0.312) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.657 (+/-0.311) for {'C': 3630780.5477010156, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.691 (+/-0.295) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.693 (+/-0.298) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.693 (+/-0.302) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.694 (+/-0.303) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.724 (+/-0.313) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.706 (+/-0.338) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.680 (+/-0.365) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.655 (+/-0.310) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.656 (+/-0.310) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.657 (+/-0.312) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.657 (+/-0.311) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.691 (+/-0.296) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.693 (+/-0.298) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.693 (+/-0.302) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.694 (+/-0.303) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.707 (+/-0.340) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.681 (+/-0.363) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.680 (+/-0.365) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.655 (+/-0.310) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.656 (+/-0.310) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.657 (+/-0.312) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.657 (+/-0.311) for {'C': 4365158.322401657, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.692 (+/-0.297) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.693 (+/-0.298) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.694 (+/-0.302) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.694 (+/-0.304) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.707 (+/-0.340) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.681 (+/-0.363) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.680 (+/-0.365) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.655 (+/-0.310) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.656 (+/-0.310) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.657 (+/-0.312) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.658 (+/-0.311) for {'C': 4786300.923226383, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.691 (+/-0.298) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.692 (+/-0.297) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.694 (+/-0.303) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.693 (+/-0.304) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.707 (+/-0.340) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.680 (+/-0.364) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.680 (+/-0.366) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.655 (+/-0.310) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.656 (+/-0.309) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.656 (+/-0.312) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.658 (+/-0.311) for {'C': 5248074.602497729, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.691 (+/-0.298) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.693 (+/-0.298) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.694 (+/-0.302) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.693 (+/-0.303) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.707 (+/-0.340) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.680 (+/-0.364) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.680 (+/-0.366) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.655 (+/-0.310) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.656 (+/-0.309) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.656 (+/-0.311) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.657 (+/-0.312) for {'C': 5754399.373371561, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.691 (+/-0.298) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.693 (+/-0.298) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.5848931924611143e-06}
0.694 (+/-0.303) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095823e-06}
0.693 (+/-0.303) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.706 (+/-0.340) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.681 (+/-0.364) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.680 (+/-0.366) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.5848931924611134e-05}
0.656 (+/-0.309) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095812e-05}
0.656 (+/-0.308) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534976e-05}
0.657 (+/-0.311) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801933e-05}
0.658 (+/-0.312) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 0.00010000000000000005}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.683 (+/-0.296) for {'C': 0.1, 'kernel': 'linear'}
0.698 (+/-0.306) for {'C': 1.0, 'kernel': 'linear'}
0.694 (+/-0.301) for {'C': 10.0, 'kernel': 'linear'}
0.656 (+/-0.311) for {'C': 100.0, 'kernel': 'linear'}
0.658 (+/-0.312) for {'C': 1000.0, 'kernel': 'linear'}
0.659 (+/-0.313) for {'C': 10000.0, 'kernel': 'linear'}
0.659 (+/-0.313) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.05 0.17 0.07 6
1 0.99 0.97 0.98 623
avg / total 0.98 0.96 0.97 629
本轮grid search结果,得到最好的参数选择是: {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.7405417800313299
第0轮loop中,tunemodel函数得到的最优参数是: ([{'C': array([2511886.43150958, 2558585.88690565, 2606153.5499989 ,
2654605.56197554, 2703958.36410884, 2754228.70333816,
2805433.63795172, 2857590.54337495, 2910717.11806661,
2964831.38952434, 3019951.72040201]), 'kernel': ['rbf'], 'gamma': array([3.98107171e-06, 4.36515832e-06, 4.78630092e-06, 5.24807460e-06,
5.75439937e-06, 6.30957344e-06, 6.91830971e-06, 7.58577575e-06,
8.31763771e-06, 9.12010839e-06, 1.00000000e-05])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}], {'C': 2754228.70333817, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}, 0.7405417800313299)
这是第0次迭代微调C和gamma。
第0次迭代,得到delta: [1.22684300e+06 3.69042656e-06]
执行tunemodel()函数前,使用的grid search parameter是:
[{'C': array([2511886.43150958, 2558585.88690565, 2606153.5499989 ,
2654605.56197554, 2703958.36410884, 2754228.70333816,
2805433.63795172, 2857590.54337495, 2910717.11806661,
2964831.38952434, 3019951.72040201]), 'kernel': ['rbf'], 'gamma': array([3.98107171e-06, 4.36515832e-06, 4.78630092e-06, 5.24807460e-06,
5.75439937e-06, 6.30957344e-06, 6.91830971e-06, 7.58577575e-06,
8.31763771e-06, 9.12010839e-06, 1.00000000e-05])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.604 (+/-0.180) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.608 (+/-0.180) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.621 (+/-0.290) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.597 (+/-0.161) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.597 (+/-0.159) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.597 (+/-0.156) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.638 (+/-0.289) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.601 (+/-0.157) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.611 (+/-0.169) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.594 (+/-0.154) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.598 (+/-0.159) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.591 (+/-0.153) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.608 (+/-0.180) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.621 (+/-0.290) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.597 (+/-0.161) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.597 (+/-0.159) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.597 (+/-0.156) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.643 (+/-0.290) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.601 (+/-0.157) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.604 (+/-0.160) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.602 (+/-0.176) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.598 (+/-0.159) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.592 (+/-0.154) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.608 (+/-0.180) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.621 (+/-0.290) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.597 (+/-0.161) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.597 (+/-0.158) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.597 (+/-0.156) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.643 (+/-0.290) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.601 (+/-0.157) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.612 (+/-0.179) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.602 (+/-0.176) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.598 (+/-0.159) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.591 (+/-0.153) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.608 (+/-0.180) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.625 (+/-0.292) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.597 (+/-0.161) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.597 (+/-0.158) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.599 (+/-0.157) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.644 (+/-0.288) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.601 (+/-0.156) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.615 (+/-0.184) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.602 (+/-0.176) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.598 (+/-0.159) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.591 (+/-0.153) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.608 (+/-0.180) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.620 (+/-0.291) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.597 (+/-0.161) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.597 (+/-0.158) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.599 (+/-0.157) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.644 (+/-0.288) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.601 (+/-0.156) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.612 (+/-0.179) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.602 (+/-0.176) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.585 (+/-0.169) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.591 (+/-0.153) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.606 (+/-0.181) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.625 (+/-0.291) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.601 (+/-0.165) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.597 (+/-0.158) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.599 (+/-0.157) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.644 (+/-0.288) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.601 (+/-0.156) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.610 (+/-0.180) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.601 (+/-0.175) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.585 (+/-0.169) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.591 (+/-0.153) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.606 (+/-0.181) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.600 (+/-0.181) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.601 (+/-0.165) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.597 (+/-0.158) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.597 (+/-0.156) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.644 (+/-0.288) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.601 (+/-0.156) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.612 (+/-0.179) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.601 (+/-0.175) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.585 (+/-0.169) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.592 (+/-0.154) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.615 (+/-0.211) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.598 (+/-0.178) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.601 (+/-0.165) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.597 (+/-0.158) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.599 (+/-0.159) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.644 (+/-0.288) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.601 (+/-0.156) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.612 (+/-0.179) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.611 (+/-0.185) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.594 (+/-0.191) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.592 (+/-0.154) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.615 (+/-0.211) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.603 (+/-0.182) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.601 (+/-0.165) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.597 (+/-0.158) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.592 (+/-0.153) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.652 (+/-0.295) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.605 (+/-0.162) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.599 (+/-0.192) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.601 (+/-0.175) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.593 (+/-0.190) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.592 (+/-0.154) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.615 (+/-0.211) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.598 (+/-0.178) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.600 (+/-0.163) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.597 (+/-0.158) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.592 (+/-0.153) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.652 (+/-0.295) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.603 (+/-0.160) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.612 (+/-0.179) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.601 (+/-0.175) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.593 (+/-0.190) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.592 (+/-0.154) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.615 (+/-0.211) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.599 (+/-0.178) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.600 (+/-0.163) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.597 (+/-0.158) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.594 (+/-0.153) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.652 (+/-0.295) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.605 (+/-0.163) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.612 (+/-0.179) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.601 (+/-0.175) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.593 (+/-0.190) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.690 (+/-0.300) for {'C': 1.0, 'kernel': 'linear'}
0.599 (+/-0.162) for {'C': 10.0, 'kernel': 'linear'}
0.599 (+/-0.192) for {'C': 100.0, 'kernel': 'linear'}
0.580 (+/-0.187) for {'C': 1000.0, 'kernel': 'linear'}
0.590 (+/-0.191) for {'C': 10000.0, 'kernel': 'linear'}
0.588 (+/-0.193) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.695 (+/-0.302) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.696 (+/-0.305) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.695 (+/-0.301) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.696 (+/-0.304) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.696 (+/-0.304) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.695 (+/-0.301) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.696 (+/-0.307) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.696 (+/-0.305) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.696 (+/-0.306) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.695 (+/-0.303) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.679 (+/-0.291) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.695 (+/-0.302) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.696 (+/-0.305) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.695 (+/-0.301) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.696 (+/-0.304) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.696 (+/-0.304) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.695 (+/-0.301) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.696 (+/-0.308) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.696 (+/-0.305) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.679 (+/-0.294) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.695 (+/-0.303) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.679 (+/-0.291) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.695 (+/-0.302) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.696 (+/-0.305) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.695 (+/-0.301) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.695 (+/-0.304) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.695 (+/-0.304) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.695 (+/-0.301) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.696 (+/-0.308) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.696 (+/-0.305) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.680 (+/-0.294) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.695 (+/-0.303) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.679 (+/-0.291) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.695 (+/-0.302) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.696 (+/-0.305) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.695 (+/-0.301) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.695 (+/-0.304) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.695 (+/-0.304) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.695 (+/-0.301) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.697 (+/-0.308) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.696 (+/-0.304) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.680 (+/-0.294) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.695 (+/-0.303) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.679 (+/-0.291) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.695 (+/-0.302) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.696 (+/-0.305) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.694 (+/-0.300) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.695 (+/-0.304) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.695 (+/-0.304) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.695 (+/-0.301) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.697 (+/-0.308) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.696 (+/-0.304) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.680 (+/-0.294) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.695 (+/-0.304) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.662 (+/-0.311) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.695 (+/-0.302) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.696 (+/-0.306) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.695 (+/-0.301) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.696 (+/-0.304) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.695 (+/-0.304) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.695 (+/-0.301) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.697 (+/-0.308) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.696 (+/-0.304) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.679 (+/-0.294) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.695 (+/-0.303) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.662 (+/-0.311) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.695 (+/-0.302) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.696 (+/-0.306) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.694 (+/-0.301) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.696 (+/-0.304) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.695 (+/-0.304) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.695 (+/-0.301) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.697 (+/-0.308) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.696 (+/-0.304) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.680 (+/-0.294) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.695 (+/-0.303) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.662 (+/-0.311) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.695 (+/-0.302) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.696 (+/-0.307) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.694 (+/-0.301) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.696 (+/-0.304) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.695 (+/-0.304) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.695 (+/-0.301) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.697 (+/-0.308) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.696 (+/-0.304) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.680 (+/-0.294) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.696 (+/-0.306) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.662 (+/-0.312) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.695 (+/-0.302) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.696 (+/-0.307) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.695 (+/-0.302) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.696 (+/-0.304) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.695 (+/-0.304) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.678 (+/-0.288) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.697 (+/-0.308) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.696 (+/-0.305) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.663 (+/-0.314) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.695 (+/-0.303) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.662 (+/-0.311) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.695 (+/-0.302) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.696 (+/-0.307) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.694 (+/-0.301) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.696 (+/-0.304) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.695 (+/-0.304) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.678 (+/-0.288) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.697 (+/-0.308) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.696 (+/-0.304) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.680 (+/-0.294) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.695 (+/-0.303) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.662 (+/-0.311) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.695 (+/-0.302) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.696 (+/-0.307) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.694 (+/-0.302) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.696 (+/-0.304) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.695 (+/-0.304) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.679 (+/-0.290) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.697 (+/-0.308) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.696 (+/-0.305) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.680 (+/-0.294) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.695 (+/-0.303) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.662 (+/-0.311) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.699 (+/-0.307) for {'C': 1.0, 'kernel': 'linear'}
0.696 (+/-0.303) for {'C': 10.0, 'kernel': 'linear'}
0.663 (+/-0.314) for {'C': 100.0, 'kernel': 'linear'}
0.659 (+/-0.313) for {'C': 1000.0, 'kernel': 'linear'}
0.660 (+/-0.314) for {'C': 10000.0, 'kernel': 'linear'}
0.659 (+/-0.313) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6985561464259213
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.587 (+/-0.152) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.582 (+/-0.145) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.579 (+/-0.146) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.585 (+/-0.152) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.588 (+/-0.159) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.590 (+/-0.156) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.594 (+/-0.162) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.588 (+/-0.159) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.592 (+/-0.171) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.582 (+/-0.154) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.584 (+/-0.153) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.587 (+/-0.152) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.582 (+/-0.145) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.577 (+/-0.145) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.585 (+/-0.152) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.587 (+/-0.160) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.590 (+/-0.156) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.599 (+/-0.167) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.588 (+/-0.159) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.584 (+/-0.158) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.590 (+/-0.178) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.584 (+/-0.153) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.587 (+/-0.152) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.582 (+/-0.145) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.577 (+/-0.145) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.585 (+/-0.152) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.589 (+/-0.160) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.590 (+/-0.156) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.599 (+/-0.167) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.588 (+/-0.159) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.592 (+/-0.181) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.590 (+/-0.178) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.584 (+/-0.152) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.587 (+/-0.152) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.582 (+/-0.145) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.581 (+/-0.152) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.585 (+/-0.152) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.588 (+/-0.160) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.593 (+/-0.157) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.600 (+/-0.166) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.589 (+/-0.159) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.592 (+/-0.181) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.590 (+/-0.178) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.584 (+/-0.152) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.587 (+/-0.152) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.582 (+/-0.145) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.576 (+/-0.145) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.584 (+/-0.153) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.588 (+/-0.161) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.593 (+/-0.157) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.600 (+/-0.166) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.588 (+/-0.159) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.592 (+/-0.181) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.590 (+/-0.178) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.572 (+/-0.159) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.587 (+/-0.152) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.581 (+/-0.145) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.583 (+/-0.152) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.584 (+/-0.153) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.587 (+/-0.161) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.597 (+/-0.149) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.600 (+/-0.166) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.588 (+/-0.159) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.591 (+/-0.181) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.590 (+/-0.178) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.572 (+/-0.159) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.587 (+/-0.152) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.581 (+/-0.145) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.581 (+/-0.152) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.583 (+/-0.153) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.587 (+/-0.161) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.597 (+/-0.149) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.600 (+/-0.166) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.588 (+/-0.159) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.592 (+/-0.181) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.590 (+/-0.178) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.574 (+/-0.153) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.587 (+/-0.152) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.581 (+/-0.145) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.579 (+/-0.148) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.583 (+/-0.153) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.587 (+/-0.161) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.600 (+/-0.152) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.600 (+/-0.166) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.589 (+/-0.161) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.592 (+/-0.181) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.590 (+/-0.178) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.583 (+/-0.179) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.587 (+/-0.152) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.586 (+/-0.147) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.583 (+/-0.155) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.583 (+/-0.153) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.587 (+/-0.161) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.593 (+/-0.146) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.608 (+/-0.186) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.591 (+/-0.165) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.579 (+/-0.189) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.590 (+/-0.178) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.583 (+/-0.179) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.587 (+/-0.152) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.586 (+/-0.147) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.579 (+/-0.148) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.584 (+/-0.155) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.587 (+/-0.161) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.593 (+/-0.146) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.608 (+/-0.186) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.591 (+/-0.165) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.592 (+/-0.181) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.590 (+/-0.178) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.583 (+/-0.179) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.587 (+/-0.152) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.586 (+/-0.147) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.579 (+/-0.148) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.584 (+/-0.155) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.587 (+/-0.161) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.592 (+/-0.146) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.608 (+/-0.186) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.593 (+/-0.169) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.592 (+/-0.181) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.590 (+/-0.178) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.583 (+/-0.179) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.798 (+/-0.437) for {'C': 0.1, 'kernel': 'linear'}
0.660 (+/-0.219) for {'C': 1.0, 'kernel': 'linear'}
0.585 (+/-0.147) for {'C': 10.0, 'kernel': 'linear'}
0.579 (+/-0.189) for {'C': 100.0, 'kernel': 'linear'}
0.577 (+/-0.186) for {'C': 1000.0, 'kernel': 'linear'}
0.588 (+/-0.191) for {'C': 10000.0, 'kernel': 'linear'}
0.587 (+/-0.193) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.694 (+/-0.303) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.693 (+/-0.304) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.692 (+/-0.302) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.693 (+/-0.305) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.692 (+/-0.304) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.716 (+/-0.351) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.717 (+/-0.353) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.691 (+/-0.307) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.691 (+/-0.305) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.692 (+/-0.303) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.699 (+/-0.345) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.694 (+/-0.303) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.693 (+/-0.304) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.692 (+/-0.302) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.693 (+/-0.305) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.692 (+/-0.304) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.716 (+/-0.351) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.717 (+/-0.353) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.691 (+/-0.307) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.674 (+/-0.290) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.692 (+/-0.303) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.699 (+/-0.345) for {'C': 2558585.8869056464, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.694 (+/-0.303) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.693 (+/-0.304) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.692 (+/-0.302) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.693 (+/-0.305) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.692 (+/-0.304) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.716 (+/-0.352) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.717 (+/-0.353) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.691 (+/-0.307) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.674 (+/-0.291) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.692 (+/-0.303) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.699 (+/-0.346) for {'C': 2606153.5499988953, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.694 (+/-0.303) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.693 (+/-0.304) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.692 (+/-0.302) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.693 (+/-0.305) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.692 (+/-0.304) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.716 (+/-0.352) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.717 (+/-0.353) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.691 (+/-0.307) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.674 (+/-0.291) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.692 (+/-0.303) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.699 (+/-0.346) for {'C': 2654605.5619755383, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.694 (+/-0.303) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.693 (+/-0.304) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.692 (+/-0.302) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.693 (+/-0.305) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.692 (+/-0.305) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.716 (+/-0.352) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.717 (+/-0.353) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.690 (+/-0.307) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.674 (+/-0.291) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.691 (+/-0.304) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.682 (+/-0.367) for {'C': 2703958.364108842, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.694 (+/-0.303) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.693 (+/-0.304) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.692 (+/-0.302) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.693 (+/-0.305) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.692 (+/-0.304) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.741 (+/-0.317) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.717 (+/-0.353) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.690 (+/-0.307) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.674 (+/-0.291) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.691 (+/-0.304) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.682 (+/-0.367) for {'C': 2754228.7033381634, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.694 (+/-0.303) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.693 (+/-0.304) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.692 (+/-0.302) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.693 (+/-0.304) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.692 (+/-0.304) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.741 (+/-0.317) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.717 (+/-0.353) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.690 (+/-0.306) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.674 (+/-0.290) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.691 (+/-0.304) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.707 (+/-0.340) for {'C': 2805433.6379517163, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.694 (+/-0.303) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.693 (+/-0.304) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.692 (+/-0.302) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.693 (+/-0.304) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.692 (+/-0.304) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.741 (+/-0.317) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.717 (+/-0.353) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.690 (+/-0.307) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.674 (+/-0.291) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.691 (+/-0.304) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.707 (+/-0.340) for {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.694 (+/-0.303) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.718 (+/-0.352) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.692 (+/-0.302) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.693 (+/-0.304) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.692 (+/-0.304) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.724 (+/-0.314) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.717 (+/-0.353) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.690 (+/-0.307) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.657 (+/-0.310) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.691 (+/-0.304) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.707 (+/-0.340) for {'C': 2910717.1180666056, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.694 (+/-0.303) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.718 (+/-0.352) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.692 (+/-0.302) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.693 (+/-0.304) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.691 (+/-0.305) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.724 (+/-0.314) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.717 (+/-0.353) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.690 (+/-0.307) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.674 (+/-0.291) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.691 (+/-0.304) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.707 (+/-0.340) for {'C': 2964831.389524342, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.694 (+/-0.303) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 3.981071705534978e-06}
0.718 (+/-0.352) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 4.365158322401668e-06}
0.692 (+/-0.302) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 4.786300923226383e-06}
0.693 (+/-0.304) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 5.248074602497729e-06}
0.691 (+/-0.305) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 5.754399373371576e-06}
0.724 (+/-0.314) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
0.717 (+/-0.353) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 6.918309709189363e-06}
0.690 (+/-0.307) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 7.585775750291841e-06}
0.674 (+/-0.291) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 8.317637711026718e-06}
0.691 (+/-0.304) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 9.12010839355909e-06}
0.707 (+/-0.340) for {'C': 3019951.7204020144, 'kernel': 'rbf', 'gamma': 9.999999999999996e-06}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.683 (+/-0.296) for {'C': 0.1, 'kernel': 'linear'}
0.698 (+/-0.306) for {'C': 1.0, 'kernel': 'linear'}
0.694 (+/-0.301) for {'C': 10.0, 'kernel': 'linear'}
0.656 (+/-0.311) for {'C': 100.0, 'kernel': 'linear'}
0.658 (+/-0.312) for {'C': 1000.0, 'kernel': 'linear'}
0.659 (+/-0.313) for {'C': 10000.0, 'kernel': 'linear'}
0.659 (+/-0.313) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.05 0.17 0.07 6
1 0.99 0.97 0.98 623
avg / total 0.98 0.96 0.97 629
本轮grid search结果,得到最好的参数选择是: {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.74070255173551
第1轮loop中,tunemodel函数得到的最优参数是: ([{'C': array([2805433.63795172, 2815788.29971032, 2826181.17981016,
2836612.41931252, 2847082.15979929, 2857590.54337495,
2868137.71266847, 2878723.81083525, 2889348.98155908,
2900013.36905407, 2910717.11806661]), 'kernel': ['rbf'], 'gamma': array([5.75439937e-06, 5.86138165e-06, 5.97035287e-06, 6.08135001e-06,
6.19441075e-06, 6.30957344e-06, 6.42687717e-06, 6.54636174e-06,
6.66806769e-06, 6.79203633e-06, 6.91830971e-06])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}], {'C': 2857590.5433749487, 'kernel': 'rbf', 'gamma': 6.309573444801928e-06}, 0.74070255173551)
这是第1次迭代微调C和gamma。
第1次迭代,得到delta: [103361.84003678 0. ]
SVC模型clf_op_iter的参数是: {'kernel': 'rbf', 'decision_function_shape': 'ovr', 'class_weight': {-1: 15}, 'tol': 0.001, 'gamma': 6.309573444801928e-06, 'random_state': 0, 'cache_size': 200, 'verbose': False, 'degree': 3, 'C': 2857590.5433749487, 'probability': False, 'shrinking': True, 'coef0': 0.0, 'max_iter': -1}
训练模型SVC对训练样本的预测准确率: 0.9397590361445783
测试集中,预测为舞弊样本的有: (array([ 8, 10, 11, 12, 13, 14, 17, 19, 20, 21, 22,
24, 25, 30, 31, 32, 34, 35, 36, 37, 38, 39,
40, 41, 42, 43, 44, 45, 46, 47, 51, 52, 53,
54, 57, 58, 59, 60, 61, 62, 64, 65, 66, 67,
71, 72, 73, 74, 75, 78, 79, 80, 88, 90, 91,
92, 93, 107, 108, 109, 110, 111, 112, 113, 114, 115,
116, 117, 118, 119, 120, 121, 123, 124, 126, 127, 128,
129, 135, 136, 141, 142, 143, 144, 145, 146, 147, 148,
149, 150, 151, 154, 155, 156, 157, 158, 162, 163, 165,
166, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185,
186, 187, 190, 191, 192, 193, 195, 196, 197, 198, 199,
200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 212,
213, 214, 215, 216, 225, 230, 231, 232, 233, 235, 236,
237, 238, 239, 242, 243, 244, 245, 246, 247, 251, 252,
253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 264,
267, 268, 269, 270, 275, 276, 277, 278, 280, 281, 283,
284, 285, 286, 287, 288, 289, 295, 296, 298, 299, 300,
301, 302, 306, 307, 309, 313, 316, 319, 320, 322, 323,
324, 325, 330, 331, 332, 333, 334, 337, 338, 339, 340,
347, 348, 349, 351, 353, 354, 356, 357, 358, 359, 360,
361, 362, 363, 364, 365, 366, 367, 368, 369, 370, 372,
383, 384, 385, 386, 387, 388, 391, 392, 395, 399, 403,
404, 410, 411, 413, 414, 419, 420, 421, 422, 423, 429,
430, 431, 432, 433, 434, 436, 438, 439, 440, 441, 442,
443, 444, 446, 447, 448, 449, 450, 454, 455, 456, 457,
460, 461, 462, 463, 464, 465, 466, 467, 468, 469, 471,
472, 473, 474, 475, 476, 477, 478, 479, 480, 481, 482,
483, 484, 485, 486, 487, 488, 489, 490, 491, 492, 493,
496, 500, 501, 502, 503, 508, 509, 510, 511, 513, 514,
515, 516, 517, 529, 532, 534, 535, 540, 541, 543, 544,
545, 546, 549, 550, 551, 552, 555, 557, 558, 560, 561,
562, 563, 564, 565, 566, 567, 568, 570, 571, 572, 573,
578, 582, 583, 584, 586, 587, 589, 590, 591, 592, 593,
594, 595, 599, 600, 601, 610, 611, 612, 613, 614, 615,
616, 617, 618, 619, 620, 621, 622, 623, 624, 627, 628,
629, 630, 631, 633, 634, 635, 641, 643, 646, 651, 652,
653, 654, 655, 656, 657, 658, 659, 660, 661, 662, 663,
664, 665, 666, 667, 668, 669, 674, 675, 676, 677, 684,
686, 687, 688, 692, 694, 695, 696, 697, 698, 699, 701,
702, 703, 706, 707, 708, 709, 710, 711, 712, 713, 714,
715, 716, 717, 718, 719, 720, 721, 722, 723, 724, 725,
727, 729, 735, 736, 742, 743, 750, 751, 752, 753, 754,
756, 757, 758, 759, 760, 761, 769, 770, 774, 775, 776,
777, 778, 785, 786, 787, 788, 789, 790, 792, 793, 794,
795, 797, 806, 807, 810, 811, 812, 814, 815, 816, 817,
818, 819, 820, 825, 826, 827, 828, 829, 830, 835, 840,
841, 843, 844, 845, 850, 851, 853, 854, 856, 857, 859,
860, 861, 862, 864, 872, 874, 875, 876, 877, 878, 879,
880, 881, 882, 883, 884, 885, 886, 892, 893, 897, 898,
900, 901, 902, 903, 904, 905, 906, 910, 911, 912, 913,
914, 919, 923, 924, 925, 927, 928, 929, 930, 931, 932,
933, 934, 935, 946, 947, 948, 949, 950, 951, 952, 953,
954, 955, 956, 958, 959, 960, 961, 962, 963, 964, 965,
966, 967, 970, 971, 976, 977, 978, 979, 980, 981, 982,
983, 984, 985, 992, 993, 994, 995, 998, 1002, 1003, 1004,
1005, 1006, 1008, 1009, 1011, 1012, 1013, 1014, 1015, 1016, 1017,
1021, 1022, 1030, 1035, 1036, 1037, 1039, 1040, 1041, 1046, 1047,
1048, 1049, 1050, 1051, 1055, 1056, 1057, 1060, 1061, 1065, 1067,
1068, 1071, 1073, 1074, 1083, 1085, 1086, 1089, 1092, 1093, 1094,
1095, 1096, 1097, 1098, 1100, 1101, 1102, 1103, 1105, 1106, 1107,
1110, 1112, 1113, 1114, 1117, 1118, 1119, 1121, 1122, 1123, 1124,
1125, 1127, 1129, 1131, 1132, 1134, 1135, 1137, 1140, 1141, 1142,
1144, 1148, 1153, 1154, 1156, 1158, 1160, 1164, 1167, 1168, 1171,
1172, 1175, 1180, 1183, 1184, 1191, 1194, 1196, 1200, 1204, 1205,
1207, 1208, 1211, 1215, 1216, 1217, 1218, 1219, 1220, 1222, 1227,
1230, 1231, 1232, 1233, 1234, 1236, 1237, 1238, 1239, 1240, 1241,
1242, 1243, 1244, 1245, 1246, 1247, 1248, 1249, 1250, 1251, 1252,
1254, 1255, 1256], dtype=int64),)
测试集中,实际为舞弊样本的有: (array([1246, 1247, 1248, 1249, 1250, 1251, 1252, 1253, 1254, 1255, 1256],
dtype=int64),)
预测的舞弊样本数目: 740
训练模型SVC对测试样本的预测准确率: 0.47200566973777464
以上是第43次特征筛选。
第43次特征筛选,AUC值是: 0.6616080548664818
X_train_iter_svc.shape is: (1257, 9)
X_test_iter_svc.shape is: (1257, 9)
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.722 (+/-0.473) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.572 (+/-0.294) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.685 (+/-0.297) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.585 (+/-0.229) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.698 (+/-0.306) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6983958900156649
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.660 (+/-0.219) for {'C': 1.0, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.565 (+/-0.151) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.698 (+/-0.306) for {'C': 1.0, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.653 (+/-0.324) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6982356336054085
RBF的粗grid search最佳超参: {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001} RBF的粗grid search最高分值: 0.6983958900156649
linear的粗grid search最佳超参: {'C': 1.0, 'kernel': 'linear'} linear的粗grid search最高分值: 0.6982356336054085
粗grid search得到的parameter是:
[{'C': array([1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02, 1.e+03, 1.e+04, 1.e+05,
1.e+06, 1.e+07, 1.e+08]), 'kernel': ['rbf'], 'gamma': array([1.e-08, 1.e-07, 1.e-06, 1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01,
1.e+00, 1.e+01, 1.e+02])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.848 (+/-0.459) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.722 (+/-0.473) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.848 (+/-0.459) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.685 (+/-0.297) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.714 (+/-0.474) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.848 (+/-0.459) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.685 (+/-0.297) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.660 (+/-0.390) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.319) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.848 (+/-0.459) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.685 (+/-0.297) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.620 (+/-0.225) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.610 (+/-0.321) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.572 (+/-0.294) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.848 (+/-0.459) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.660 (+/-0.219) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.620 (+/-0.225) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.598 (+/-0.303) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.580 (+/-0.225) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.570 (+/-0.293) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.848 (+/-0.459) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.660 (+/-0.219) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.620 (+/-0.225) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.575 (+/-0.185) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.587 (+/-0.192) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.563 (+/-0.195) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.570 (+/-0.293) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.773 (+/-0.417) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.690 (+/-0.300) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.637 (+/-0.210) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.585 (+/-0.178) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.601 (+/-0.182) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.568 (+/-0.198) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.563 (+/-0.195) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.570 (+/-0.293) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.848 (+/-0.459) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.599 (+/-0.244) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.677 (+/-0.300) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.610 (+/-0.294) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.572 (+/-0.142) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.578 (+/-0.156) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.577 (+/-0.192) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.563 (+/-0.195) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.570 (+/-0.293) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.790 (+/-0.443) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.638 (+/-0.380) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.664 (+/-0.318) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.604 (+/-0.297) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.575 (+/-0.155) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.579 (+/-0.156) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.574 (+/-0.193) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.563 (+/-0.195) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.570 (+/-0.293) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.685 (+/-0.297) for {'C': 1.0, 'kernel': 'linear'}
0.641 (+/-0.211) for {'C': 10.0, 'kernel': 'linear'}
0.584 (+/-0.188) for {'C': 100.0, 'kernel': 'linear'}
0.576 (+/-0.185) for {'C': 1000.0, 'kernel': 'linear'}
0.599 (+/-0.183) for {'C': 10000.0, 'kernel': 'linear'}
0.602 (+/-0.181) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.658 (+/-0.217) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.002) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.498 (+/-0.005) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.658 (+/-0.217) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.698 (+/-0.306) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.005) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.658 (+/-0.217) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.306) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.613 (+/-0.236) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.497 (+/-0.004) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.002) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.658 (+/-0.217) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.698 (+/-0.306) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.640 (+/-0.234) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.585 (+/-0.229) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.497 (+/-0.005) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.002) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.658 (+/-0.217) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.698 (+/-0.306) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.640 (+/-0.234) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.636 (+/-0.231) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.586 (+/-0.233) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.585 (+/-0.228) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.497 (+/-0.005) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.002) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.658 (+/-0.217) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.698 (+/-0.306) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.640 (+/-0.234) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.660 (+/-0.308) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.628 (+/-0.226) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.585 (+/-0.231) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.585 (+/-0.227) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.497 (+/-0.005) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.002) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.683 (+/-0.296) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.699 (+/-0.307) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.673 (+/-0.238) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.677 (+/-0.288) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.694 (+/-0.305) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.592 (+/-0.262) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.585 (+/-0.231) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.585 (+/-0.227) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.497 (+/-0.005) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.002) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.658 (+/-0.217) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.628 (+/-0.272) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.682 (+/-0.294) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.677 (+/-0.284) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.672 (+/-0.289) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.687 (+/-0.312) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.266) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.585 (+/-0.231) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.585 (+/-0.227) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.497 (+/-0.005) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.002) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.699 (+/-0.306) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.619 (+/-0.210) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.665 (+/-0.315) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.675 (+/-0.282) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.687 (+/-0.309) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.688 (+/-0.311) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.265) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.585 (+/-0.231) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.585 (+/-0.227) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.497 (+/-0.005) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.002) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.698 (+/-0.306) for {'C': 1.0, 'kernel': 'linear'}
0.656 (+/-0.214) for {'C': 10.0, 'kernel': 'linear'}
0.662 (+/-0.310) for {'C': 100.0, 'kernel': 'linear'}
0.660 (+/-0.309) for {'C': 1000.0, 'kernel': 'linear'}
0.692 (+/-0.308) for {'C': 10000.0, 'kernel': 'linear'}
0.691 (+/-0.313) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6990374309506142
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.660 (+/-0.291) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.747 (+/-0.502) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.646 (+/-0.204) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.689 (+/-0.436) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.747 (+/-0.502) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.646 (+/-0.204) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.643 (+/-0.202) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.656 (+/-0.449) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.747 (+/-0.502) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.646 (+/-0.204) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.639 (+/-0.202) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.574 (+/-0.187) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.596 (+/-0.307) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.646 (+/-0.204) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.639 (+/-0.202) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.580 (+/-0.180) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.568 (+/-0.175) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.572 (+/-0.294) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.655 (+/-0.213) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.639 (+/-0.202) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.581 (+/-0.180) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.572 (+/-0.186) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.560 (+/-0.195) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.570 (+/-0.293) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.747 (+/-0.502) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.680 (+/-0.295) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.630 (+/-0.189) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.582 (+/-0.181) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.574 (+/-0.185) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.562 (+/-0.164) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.563 (+/-0.195) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.570 (+/-0.293) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.748 (+/-0.389) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.676 (+/-0.294) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.600 (+/-0.300) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.564 (+/-0.140) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.574 (+/-0.157) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.568 (+/-0.198) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.563 (+/-0.195) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.570 (+/-0.293) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.823 (+/-0.451) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.586 (+/-0.312) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.637 (+/-0.298) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.561 (+/-0.155) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.563 (+/-0.144) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.573 (+/-0.161) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.577 (+/-0.192) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.563 (+/-0.195) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.570 (+/-0.293) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.790 (+/-0.443) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.556 (+/-0.298) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.608 (+/-0.198) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.560 (+/-0.155) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.570 (+/-0.160) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.572 (+/-0.161) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.574 (+/-0.193) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.563 (+/-0.195) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.570 (+/-0.293) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.798 (+/-0.437) for {'C': 0.1, 'kernel': 'linear'}
0.660 (+/-0.219) for {'C': 1.0, 'kernel': 'linear'}
0.600 (+/-0.179) for {'C': 10.0, 'kernel': 'linear'}
0.577 (+/-0.179) for {'C': 100.0, 'kernel': 'linear'}
0.565 (+/-0.151) for {'C': 1000.0, 'kernel': 'linear'}
0.575 (+/-0.158) for {'C': 10000.0, 'kernel': 'linear'}
0.576 (+/-0.157) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.657 (+/-0.214) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.238) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.698 (+/-0.306) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.006) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.498 (+/-0.005) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.617 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.306) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.681 (+/-0.293) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.611 (+/-0.234) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.494 (+/-0.009) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.617 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.698 (+/-0.306) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.698 (+/-0.306) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.627 (+/-0.219) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.608 (+/-0.243) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.497 (+/-0.004) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.002) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.698 (+/-0.306) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.698 (+/-0.306) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.675 (+/-0.286) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.609 (+/-0.241) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.585 (+/-0.229) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.497 (+/-0.005) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.002) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.698 (+/-0.306) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.698 (+/-0.306) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.675 (+/-0.286) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.633 (+/-0.235) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.582 (+/-0.234) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.585 (+/-0.228) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.497 (+/-0.005) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.002) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.617 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.698 (+/-0.306) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.681 (+/-0.293) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.676 (+/-0.287) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.658 (+/-0.312) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.597 (+/-0.234) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.585 (+/-0.231) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.585 (+/-0.227) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.497 (+/-0.005) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.002) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.683 (+/-0.295) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.698 (+/-0.307) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.674 (+/-0.281) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.671 (+/-0.289) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.684 (+/-0.310) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.590 (+/-0.266) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.585 (+/-0.231) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.585 (+/-0.227) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.497 (+/-0.005) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.002) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.683 (+/-0.296) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.605 (+/-0.239) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.679 (+/-0.292) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.671 (+/-0.281) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.664 (+/-0.295) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.680 (+/-0.296) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.266) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.585 (+/-0.231) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.585 (+/-0.227) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.497 (+/-0.005) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.002) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.699 (+/-0.306) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.506 (+/-0.278) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.678 (+/-0.290) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.668 (+/-0.281) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.680 (+/-0.303) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.681 (+/-0.291) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.265) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.585 (+/-0.231) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.585 (+/-0.227) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.497 (+/-0.005) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.002) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.658 (+/-0.216) for {'C': 0.1, 'kernel': 'linear'}
0.698 (+/-0.306) for {'C': 1.0, 'kernel': 'linear'}
0.679 (+/-0.291) for {'C': 10.0, 'kernel': 'linear'}
0.675 (+/-0.286) for {'C': 100.0, 'kernel': 'linear'}
0.653 (+/-0.324) for {'C': 1000.0, 'kernel': 'linear'}
0.686 (+/-0.314) for {'C': 10000.0, 'kernel': 'linear'}
0.686 (+/-0.313) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6990374309506142
发现最优参数C为原先的最大/最小值,直接重新设置超参。
发现最优参数gamma为原先的最大/最小值,直接重新设置超参。
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-09}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.848 (+/-0.459) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.685 (+/-0.297) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-09}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.848 (+/-0.459) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.660 (+/-0.219) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.620 (+/-0.225) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-09}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.848 (+/-0.459) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.660 (+/-0.219) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.620 (+/-0.225) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.575 (+/-0.185) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-09}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.773 (+/-0.417) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.690 (+/-0.300) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.637 (+/-0.210) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.585 (+/-0.178) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.601 (+/-0.182) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-09}
0.848 (+/-0.459) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.599 (+/-0.244) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.677 (+/-0.300) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.610 (+/-0.294) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.572 (+/-0.142) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.578 (+/-0.156) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-10}
0.848 (+/-0.459) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-09}
0.790 (+/-0.443) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.638 (+/-0.380) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.664 (+/-0.318) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.604 (+/-0.297) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.575 (+/-0.155) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.579 (+/-0.156) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 1e-11}
0.848 (+/-0.459) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 1e-10}
0.790 (+/-0.443) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 1e-09}
0.787 (+/-0.446) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.638 (+/-0.381) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.664 (+/-0.318) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.604 (+/-0.297) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.576 (+/-0.156) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.577 (+/-0.157) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 1e-12}
0.848 (+/-0.459) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 1e-11}
0.790 (+/-0.443) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 1e-10}
0.787 (+/-0.446) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 1e-09}
0.787 (+/-0.446) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.636 (+/-0.382) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.664 (+/-0.318) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.604 (+/-0.297) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.576 (+/-0.156) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.578 (+/-0.157) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 1e-13}
0.848 (+/-0.459) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 1e-12}
0.790 (+/-0.443) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 1e-11}
0.787 (+/-0.446) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 1e-10}
0.787 (+/-0.446) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 1e-09}
0.787 (+/-0.446) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.638 (+/-0.381) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.664 (+/-0.318) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.604 (+/-0.297) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.576 (+/-0.156) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.577 (+/-0.157) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.848 (+/-0.459) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 1e-13}
0.790 (+/-0.443) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 1e-12}
0.787 (+/-0.446) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 1e-11}
0.787 (+/-0.446) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 1e-10}
0.787 (+/-0.446) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 1e-09}
0.787 (+/-0.446) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.638 (+/-0.381) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.664 (+/-0.318) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.604 (+/-0.297) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.576 (+/-0.156) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.576 (+/-0.157) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.790 (+/-0.443) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 1e-13}
0.787 (+/-0.446) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 1e-12}
0.787 (+/-0.446) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 1e-11}
0.787 (+/-0.446) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 1e-10}
0.787 (+/-0.446) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 1e-09}
0.787 (+/-0.446) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.638 (+/-0.381) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.664 (+/-0.318) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.604 (+/-0.297) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.576 (+/-0.156) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.578 (+/-0.157) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.685 (+/-0.297) for {'C': 1.0, 'kernel': 'linear'}
0.641 (+/-0.211) for {'C': 10.0, 'kernel': 'linear'}
0.584 (+/-0.188) for {'C': 100.0, 'kernel': 'linear'}
0.576 (+/-0.185) for {'C': 1000.0, 'kernel': 'linear'}
0.599 (+/-0.183) for {'C': 10000.0, 'kernel': 'linear'}
0.602 (+/-0.181) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-09}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.658 (+/-0.217) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.698 (+/-0.306) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-09}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.658 (+/-0.217) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.698 (+/-0.306) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.640 (+/-0.234) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-09}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.658 (+/-0.217) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.698 (+/-0.306) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.640 (+/-0.234) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.660 (+/-0.308) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-09}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.683 (+/-0.296) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.699 (+/-0.307) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.673 (+/-0.238) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.677 (+/-0.288) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.694 (+/-0.305) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-09}
0.658 (+/-0.217) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.628 (+/-0.272) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.682 (+/-0.294) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.677 (+/-0.284) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.672 (+/-0.289) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.687 (+/-0.312) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-10}
0.658 (+/-0.217) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-09}
0.699 (+/-0.306) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.619 (+/-0.210) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.665 (+/-0.315) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.675 (+/-0.282) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.687 (+/-0.309) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.688 (+/-0.311) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 1e-11}
0.658 (+/-0.217) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 1e-10}
0.699 (+/-0.306) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 1e-09}
0.699 (+/-0.305) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.616 (+/-0.212) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.665 (+/-0.315) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.675 (+/-0.282) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.686 (+/-0.310) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.687 (+/-0.310) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 1e-12}
0.658 (+/-0.217) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 1e-11}
0.699 (+/-0.306) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 1e-10}
0.699 (+/-0.305) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 1e-09}
0.699 (+/-0.305) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.615 (+/-0.211) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.665 (+/-0.315) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.675 (+/-0.282) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.686 (+/-0.308) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.687 (+/-0.311) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 1e-13}
0.658 (+/-0.217) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 1e-12}
0.699 (+/-0.306) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 1e-11}
0.699 (+/-0.305) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 1e-10}
0.699 (+/-0.305) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 1e-09}
0.699 (+/-0.305) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.615 (+/-0.212) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.665 (+/-0.315) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.675 (+/-0.282) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.686 (+/-0.310) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.687 (+/-0.310) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.658 (+/-0.217) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 1e-13}
0.699 (+/-0.306) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 1e-12}
0.699 (+/-0.305) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 1e-11}
0.699 (+/-0.305) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 1e-10}
0.699 (+/-0.305) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 1e-09}
0.699 (+/-0.305) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.615 (+/-0.212) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.665 (+/-0.315) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.675 (+/-0.282) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.686 (+/-0.310) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.687 (+/-0.308) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.699 (+/-0.306) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 1e-13}
0.699 (+/-0.305) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 1e-12}
0.699 (+/-0.305) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 1e-11}
0.699 (+/-0.305) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 1e-10}
0.699 (+/-0.305) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 1e-09}
0.699 (+/-0.305) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.617 (+/-0.208) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.665 (+/-0.315) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.675 (+/-0.282) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.686 (+/-0.309) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.687 (+/-0.311) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.698 (+/-0.306) for {'C': 1.0, 'kernel': 'linear'}
0.656 (+/-0.214) for {'C': 10.0, 'kernel': 'linear'}
0.662 (+/-0.310) for {'C': 100.0, 'kernel': 'linear'}
0.660 (+/-0.309) for {'C': 1000.0, 'kernel': 'linear'}
0.692 (+/-0.308) for {'C': 10000.0, 'kernel': 'linear'}
0.691 (+/-0.313) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6990374309506142
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-09}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.747 (+/-0.502) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.646 (+/-0.204) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.639 (+/-0.202) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-09}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.747 (+/-0.502) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.655 (+/-0.213) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.639 (+/-0.202) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.581 (+/-0.180) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-09}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.747 (+/-0.502) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.680 (+/-0.295) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.630 (+/-0.189) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.582 (+/-0.181) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.574 (+/-0.185) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-09}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.748 (+/-0.389) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.676 (+/-0.294) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.600 (+/-0.300) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.564 (+/-0.140) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.574 (+/-0.157) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-10}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-09}
0.823 (+/-0.451) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.586 (+/-0.312) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.637 (+/-0.298) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.561 (+/-0.155) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.563 (+/-0.144) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.573 (+/-0.161) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-11}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-10}
0.823 (+/-0.451) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-09}
0.790 (+/-0.443) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.556 (+/-0.298) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.608 (+/-0.198) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.560 (+/-0.155) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.570 (+/-0.160) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.572 (+/-0.161) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 1e-12}
0.496 (+/-0.001) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 1e-11}
0.823 (+/-0.451) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 1e-10}
0.790 (+/-0.443) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 1e-09}
0.790 (+/-0.443) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.552 (+/-0.298) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.607 (+/-0.199) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.561 (+/-0.155) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.572 (+/-0.160) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.572 (+/-0.161) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 1e-13}
0.496 (+/-0.001) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 1e-12}
0.823 (+/-0.451) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 1e-11}
0.790 (+/-0.443) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 1e-10}
0.790 (+/-0.443) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 1e-09}
0.790 (+/-0.443) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.552 (+/-0.298) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.607 (+/-0.199) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.561 (+/-0.155) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.572 (+/-0.160) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.572 (+/-0.161) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 1e-13}
0.823 (+/-0.451) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 1e-12}
0.790 (+/-0.443) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 1e-11}
0.790 (+/-0.443) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 1e-10}
0.790 (+/-0.443) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 1e-09}
0.790 (+/-0.443) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.552 (+/-0.298) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.607 (+/-0.199) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.561 (+/-0.155) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.572 (+/-0.160) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.572 (+/-0.161) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.823 (+/-0.451) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 1e-13}
0.790 (+/-0.443) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 1e-12}
0.790 (+/-0.443) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 1e-11}
0.790 (+/-0.443) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 1e-10}
0.790 (+/-0.443) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 1e-09}
0.790 (+/-0.443) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.552 (+/-0.298) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.607 (+/-0.199) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.561 (+/-0.155) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.572 (+/-0.160) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.572 (+/-0.161) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.790 (+/-0.443) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 1e-13}
0.790 (+/-0.443) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 1e-12}
0.790 (+/-0.443) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 1e-11}
0.790 (+/-0.443) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 1e-10}
0.790 (+/-0.443) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 1e-09}
0.790 (+/-0.443) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.554 (+/-0.296) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.605 (+/-0.200) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.561 (+/-0.155) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.572 (+/-0.160) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.572 (+/-0.161) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.798 (+/-0.437) for {'C': 0.1, 'kernel': 'linear'}
0.660 (+/-0.219) for {'C': 1.0, 'kernel': 'linear'}
0.600 (+/-0.179) for {'C': 10.0, 'kernel': 'linear'}
0.577 (+/-0.179) for {'C': 100.0, 'kernel': 'linear'}
0.565 (+/-0.151) for {'C': 1000.0, 'kernel': 'linear'}
0.575 (+/-0.158) for {'C': 10000.0, 'kernel': 'linear'}
0.576 (+/-0.157) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-09}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.617 (+/-0.238) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.698 (+/-0.306) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.698 (+/-0.306) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-09}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.617 (+/-0.238) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.698 (+/-0.306) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.698 (+/-0.306) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.675 (+/-0.286) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-09}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.617 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.698 (+/-0.306) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.681 (+/-0.293) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.676 (+/-0.287) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.658 (+/-0.312) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-09}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.683 (+/-0.295) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.698 (+/-0.307) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.674 (+/-0.281) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.671 (+/-0.289) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.684 (+/-0.310) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-10}
0.500 (+/-0.000) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-09}
0.683 (+/-0.296) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.605 (+/-0.239) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.679 (+/-0.292) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.671 (+/-0.281) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.664 (+/-0.295) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.680 (+/-0.296) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-11}
0.500 (+/-0.000) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-10}
0.683 (+/-0.296) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-09}
0.699 (+/-0.306) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.506 (+/-0.278) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.678 (+/-0.290) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.668 (+/-0.281) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.680 (+/-0.303) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.681 (+/-0.291) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 1e-12}
0.500 (+/-0.000) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 1e-11}
0.683 (+/-0.296) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 1e-10}
0.699 (+/-0.306) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 1e-09}
0.699 (+/-0.306) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.499 (+/-0.283) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.678 (+/-0.289) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.668 (+/-0.282) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.680 (+/-0.303) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.681 (+/-0.292) for {'C': 1000000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 1e-13}
0.500 (+/-0.000) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 1e-12}
0.683 (+/-0.296) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 1e-11}
0.699 (+/-0.306) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 1e-10}
0.699 (+/-0.306) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 1e-09}
0.699 (+/-0.306) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.498 (+/-0.283) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.678 (+/-0.289) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.668 (+/-0.282) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.680 (+/-0.303) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.681 (+/-0.292) for {'C': 10000000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 1e-13}
0.683 (+/-0.296) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 1e-12}
0.699 (+/-0.306) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 1e-11}
0.699 (+/-0.306) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 1e-10}
0.699 (+/-0.306) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 1e-09}
0.699 (+/-0.306) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.498 (+/-0.284) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.678 (+/-0.289) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.668 (+/-0.282) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.680 (+/-0.303) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.681 (+/-0.292) for {'C': 100000000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.683 (+/-0.296) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 1e-13}
0.699 (+/-0.306) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 1e-12}
0.699 (+/-0.306) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 1e-11}
0.699 (+/-0.306) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 1e-10}
0.699 (+/-0.306) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 1e-09}
0.699 (+/-0.306) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.498 (+/-0.284) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.678 (+/-0.289) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.668 (+/-0.282) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.680 (+/-0.303) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.681 (+/-0.292) for {'C': 1000000000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.699 (+/-0.306) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 1e-13}
0.699 (+/-0.306) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 1e-12}
0.699 (+/-0.306) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 1e-11}
0.699 (+/-0.306) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 1e-10}
0.699 (+/-0.306) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 1e-09}
0.699 (+/-0.306) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.520 (+/-0.313) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.678 (+/-0.288) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.668 (+/-0.282) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.680 (+/-0.303) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.681 (+/-0.292) for {'C': 10000000000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.658 (+/-0.216) for {'C': 0.1, 'kernel': 'linear'}
0.698 (+/-0.306) for {'C': 1.0, 'kernel': 'linear'}
0.679 (+/-0.291) for {'C': 10.0, 'kernel': 'linear'}
0.675 (+/-0.286) for {'C': 100.0, 'kernel': 'linear'}
0.653 (+/-0.324) for {'C': 1000.0, 'kernel': 'linear'}
0.686 (+/-0.314) for {'C': 10000.0, 'kernel': 'linear'}
0.686 (+/-0.313) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6990374309506142
循环迭代之前,delta is: [0. 0.]
SVC模型clf_op_iter的参数是: {'kernel': 'rbf', 'decision_function_shape': 'ovr', 'class_weight': {-1: 15}, 'tol': 0.001, 'gamma': 1e-08, 'random_state': 0, 'cache_size': 200, 'verbose': False, 'degree': 3, 'C': 100000000.0, 'probability': False, 'shrinking': True, 'coef0': 0.0, 'max_iter': -1}
训练模型SVC对训练样本的预测准确率: 0.9043231750531537
测试集中,预测为舞弊样本的有: (array([ 370, 1247, 1248], dtype=int64),)
测试集中,实际为舞弊样本的有: (array([1246, 1247, 1248, 1249, 1250, 1251, 1252, 1253, 1254, 1255, 1256],
dtype=int64),)
预测的舞弊样本数目: 3
训练模型SVC对测试样本的预测准确率: 0.9036144578313253
以上是第44次特征筛选。
第44次特征筛选,AUC值是: 0.5905078067999416
X_train_iter_svc.shape is: (1257, 8)
X_test_iter_svc.shape is: (1257, 8)
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.639 (+/-0.327) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.550 (+/-0.157) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.630 (+/-0.189) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [1]
检查交叉验证集中测试集标签是否有预测不到的值: {-1}
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 1.00 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.586 (+/-0.229) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.681 (+/-0.293) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.33 0.17 0.22 6
1 0.99 1.00 0.99 623
avg / total 0.99 0.99 0.99 629
本轮grid search结果,得到最好的参数选择是: {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6810881977079727
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.630 (+/-0.189) for {'C': 1.0, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.573 (+/-0.146) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.33 0.17 0.22 6
1 0.99 1.00 0.99 623
avg / total 0.99 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.698 (+/-0.306) for {'C': 1.0, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.670 (+/-0.306) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.33 0.17 0.22 6
1 0.99 1.00 0.99 623
avg / total 0.99 0.99 0.99 629
本轮grid search结果,得到最好的参数选择是: {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.697594607964383
RBF的粗grid search最佳超参: {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001} RBF的粗grid search最高分值: 0.6810881977079727
linear的粗grid search最佳超参: {'C': 1.0, 'kernel': 'linear'} linear的粗grid search最高分值: 0.697594607964383
粗grid search得到的parameter是:
[{'C': array([1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02, 1.e+03, 1.e+04, 1.e+05,
1.e+06, 1.e+07, 1.e+08]), 'kernel': ['rbf'], 'gamma': array([1.e-08, 1.e-07, 1.e-06, 1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01,
1.e+00, 1.e+01, 1.e+02])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.327) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.689 (+/-0.436) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.635 (+/-0.198) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.689 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.689 (+/-0.436) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.630 (+/-0.189) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.635 (+/-0.326) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.564 (+/-0.195) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.689 (+/-0.436) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.630 (+/-0.189) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.603 (+/-0.195) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.609 (+/-0.322) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.550 (+/-0.157) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.689 (+/-0.436) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.630 (+/-0.189) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.599 (+/-0.192) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.577 (+/-0.153) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.580 (+/-0.225) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.547 (+/-0.154) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.689 (+/-0.436) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.643 (+/-0.202) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.603 (+/-0.195) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.573 (+/-0.152) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.621 (+/-0.312) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.561 (+/-0.195) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.547 (+/-0.154) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.639 (+/-0.285) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.634 (+/-0.190) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.620 (+/-0.184) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.577 (+/-0.155) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.594 (+/-0.178) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.547 (+/-0.171) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.561 (+/-0.195) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.547 (+/-0.154) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.797 (+/-0.492) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.624 (+/-0.212) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.623 (+/-0.184) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.580 (+/-0.155) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.595 (+/-0.177) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.592 (+/-0.168) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.553 (+/-0.167) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.561 (+/-0.195) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.547 (+/-0.154) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.779 (+/-0.452) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.648 (+/-0.320) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.614 (+/-0.182) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.577 (+/-0.154) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.618 (+/-0.201) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.592 (+/-0.172) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.552 (+/-0.167) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.561 (+/-0.195) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.547 (+/-0.154) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.643 (+/-0.202) for {'C': 1.0, 'kernel': 'linear'}
0.599 (+/-0.192) for {'C': 10.0, 'kernel': 'linear'}
0.593 (+/-0.178) for {'C': 100.0, 'kernel': 'linear'}
0.587 (+/-0.179) for {'C': 1000.0, 'kernel': 'linear'}
0.607 (+/-0.184) for {'C': 10000.0, 'kernel': 'linear'}
0.612 (+/-0.181) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [1]
检查交叉验证集中测试集标签是否有预测不到的值: {-1}
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 1.00 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.002) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.497 (+/-0.006) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.214) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.004) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.003) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.616 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.681 (+/-0.293) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.589 (+/-0.226) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.007) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.003) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.681 (+/-0.293) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.639 (+/-0.233) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.586 (+/-0.229) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.007) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.003) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.616 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.681 (+/-0.293) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.639 (+/-0.233) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.677 (+/-0.288) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.587 (+/-0.232) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.586 (+/-0.227) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.007) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.003) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.616 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.681 (+/-0.293) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.639 (+/-0.233) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.677 (+/-0.287) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.628 (+/-0.227) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.585 (+/-0.230) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.586 (+/-0.227) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.007) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.003) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.681 (+/-0.294) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.681 (+/-0.293) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.681 (+/-0.292) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.677 (+/-0.287) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.693 (+/-0.303) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.566 (+/-0.245) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.585 (+/-0.229) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.586 (+/-0.227) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.007) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.003) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.642 (+/-0.236) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.681 (+/-0.255) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.681 (+/-0.293) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.693 (+/-0.298) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.677 (+/-0.297) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.690 (+/-0.311) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.590 (+/-0.261) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.585 (+/-0.230) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.586 (+/-0.227) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.007) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.003) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.682 (+/-0.293) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.658 (+/-0.243) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.680 (+/-0.290) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.692 (+/-0.297) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.693 (+/-0.314) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.688 (+/-0.312) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.589 (+/-0.260) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.585 (+/-0.230) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.586 (+/-0.227) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.007) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.003) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.681 (+/-0.293) for {'C': 1.0, 'kernel': 'linear'}
0.639 (+/-0.233) for {'C': 10.0, 'kernel': 'linear'}
0.678 (+/-0.289) for {'C': 100.0, 'kernel': 'linear'}
0.677 (+/-0.290) for {'C': 1000.0, 'kernel': 'linear'}
0.693 (+/-0.310) for {'C': 10000.0, 'kernel': 'linear'}
0.694 (+/-0.310) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.36 0.83 0.50 6
1 1.00 0.99 0.99 623
avg / total 0.99 0.98 0.99 629
本轮grid search结果,得到最好的参数选择是: {'C': 100000.0, 'kernel': 'linear'}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6935866518262017
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.618 (+/-0.214) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.630 (+/-0.192) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.327) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.618 (+/-0.172) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.623 (+/-0.183) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.631 (+/-0.395) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.618 (+/-0.172) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.167) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.581 (+/-0.180) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.549 (+/-0.155) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.625 (+/-0.179) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.610 (+/-0.163) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.582 (+/-0.180) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.568 (+/-0.175) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.550 (+/-0.157) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.618 (+/-0.172) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.614 (+/-0.167) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.582 (+/-0.180) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.579 (+/-0.178) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.555 (+/-0.199) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.547 (+/-0.154) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.747 (+/-0.502) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.622 (+/-0.174) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.614 (+/-0.169) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.582 (+/-0.180) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.570 (+/-0.152) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.571 (+/-0.191) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.561 (+/-0.195) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.547 (+/-0.154) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.597 (+/-0.177) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.610 (+/-0.166) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.583 (+/-0.182) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.567 (+/-0.144) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.569 (+/-0.148) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.547 (+/-0.171) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.561 (+/-0.195) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.547 (+/-0.154) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.798 (+/-0.437) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.530 (+/-0.146) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.586 (+/-0.155) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.561 (+/-0.146) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.571 (+/-0.147) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.584 (+/-0.176) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.553 (+/-0.167) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.561 (+/-0.195) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.547 (+/-0.154) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.775 (+/-0.457) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.550 (+/-0.298) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.578 (+/-0.149) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.558 (+/-0.144) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.592 (+/-0.189) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.586 (+/-0.177) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.552 (+/-0.167) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.561 (+/-0.195) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.547 (+/-0.154) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.764 (+/-0.421) for {'C': 0.1, 'kernel': 'linear'}
0.630 (+/-0.189) for {'C': 1.0, 'kernel': 'linear'}
0.603 (+/-0.179) for {'C': 10.0, 'kernel': 'linear'}
0.568 (+/-0.153) for {'C': 100.0, 'kernel': 'linear'}
0.573 (+/-0.146) for {'C': 1000.0, 'kernel': 'linear'}
0.579 (+/-0.159) for {'C': 10000.0, 'kernel': 'linear'}
0.585 (+/-0.161) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [1]
检查交叉验证集中测试集标签是否有预测不到的值: {-1}
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 1.00 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.640 (+/-0.234) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.306) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.006) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.497 (+/-0.006) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.681 (+/-0.293) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.214) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.611 (+/-0.234) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.494 (+/-0.009) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.003) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.681 (+/-0.293) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.681 (+/-0.292) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.652 (+/-0.207) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.583 (+/-0.232) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.007) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.003) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.306) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.680 (+/-0.292) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.676 (+/-0.286) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.609 (+/-0.241) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.585 (+/-0.230) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.007) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.003) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.681 (+/-0.293) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.681 (+/-0.292) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.676 (+/-0.286) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.675 (+/-0.290) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.555 (+/-0.218) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.586 (+/-0.227) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.007) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.003) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.617 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.681 (+/-0.293) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.681 (+/-0.293) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.676 (+/-0.286) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.674 (+/-0.290) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.596 (+/-0.236) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.585 (+/-0.230) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.586 (+/-0.227) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.007) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.003) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.679 (+/-0.293) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.697 (+/-0.303) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.692 (+/-0.298) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.672 (+/-0.294) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.684 (+/-0.313) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.564 (+/-0.249) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.585 (+/-0.229) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.586 (+/-0.227) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.007) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.003) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.658 (+/-0.216) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.581 (+/-0.278) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.694 (+/-0.298) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.687 (+/-0.287) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.667 (+/-0.305) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.682 (+/-0.294) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.590 (+/-0.261) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.585 (+/-0.230) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.586 (+/-0.227) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.007) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.003) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.682 (+/-0.293) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.537 (+/-0.183) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.692 (+/-0.292) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.686 (+/-0.286) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.683 (+/-0.315) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.682 (+/-0.295) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.589 (+/-0.260) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.585 (+/-0.230) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.586 (+/-0.227) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.007) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.003) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.658 (+/-0.216) for {'C': 0.1, 'kernel': 'linear'}
0.698 (+/-0.306) for {'C': 1.0, 'kernel': 'linear'}
0.679 (+/-0.291) for {'C': 10.0, 'kernel': 'linear'}
0.675 (+/-0.285) for {'C': 100.0, 'kernel': 'linear'}
0.670 (+/-0.306) for {'C': 1000.0, 'kernel': 'linear'}
0.685 (+/-0.315) for {'C': 10000.0, 'kernel': 'linear'}
0.687 (+/-0.313) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.33 0.17 0.22 6
1 0.99 1.00 0.99 623
avg / total 0.99 0.99 0.99 629
本轮grid search结果,得到最好的参数选择是: {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.697594607964383
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.327) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.689 (+/-0.436) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.635 (+/-0.198) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.689 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.689 (+/-0.436) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.630 (+/-0.189) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.635 (+/-0.326) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.564 (+/-0.195) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.689 (+/-0.436) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.630 (+/-0.189) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.603 (+/-0.195) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.609 (+/-0.322) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.550 (+/-0.157) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.689 (+/-0.436) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.630 (+/-0.189) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.599 (+/-0.192) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.577 (+/-0.153) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.580 (+/-0.225) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.547 (+/-0.154) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.689 (+/-0.436) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.643 (+/-0.202) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.603 (+/-0.195) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.573 (+/-0.152) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.621 (+/-0.312) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.561 (+/-0.195) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.547 (+/-0.154) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.639 (+/-0.285) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.634 (+/-0.190) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.620 (+/-0.184) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.577 (+/-0.155) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.594 (+/-0.178) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.547 (+/-0.171) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.561 (+/-0.195) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.547 (+/-0.154) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.797 (+/-0.492) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.624 (+/-0.212) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.623 (+/-0.184) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.580 (+/-0.155) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.595 (+/-0.177) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.592 (+/-0.168) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.553 (+/-0.167) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.561 (+/-0.195) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.547 (+/-0.154) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.779 (+/-0.452) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.648 (+/-0.320) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.614 (+/-0.182) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.577 (+/-0.154) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.618 (+/-0.201) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.592 (+/-0.172) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.552 (+/-0.167) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.561 (+/-0.195) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.547 (+/-0.154) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.697 (+/-0.437) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.789 (+/-0.443) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.648 (+/-0.209) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.643 (+/-0.202) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.643 (+/-0.202) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.639 (+/-0.201) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.630 (+/-0.189) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.623 (+/-0.183) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.616 (+/-0.181) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.599 (+/-0.192) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [1]
检查交叉验证集中测试集标签是否有预测不到的值: {-1}
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 1.00 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.002) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.497 (+/-0.006) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.214) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.004) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.003) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.616 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.681 (+/-0.293) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.589 (+/-0.226) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.007) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.003) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.681 (+/-0.293) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.639 (+/-0.233) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.586 (+/-0.229) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.007) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.003) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.616 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.681 (+/-0.293) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.639 (+/-0.233) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.677 (+/-0.288) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.587 (+/-0.232) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.586 (+/-0.227) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.007) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.003) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.616 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.681 (+/-0.293) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.639 (+/-0.233) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.677 (+/-0.287) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.628 (+/-0.227) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.585 (+/-0.230) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.586 (+/-0.227) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.007) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.003) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.681 (+/-0.294) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.681 (+/-0.293) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.681 (+/-0.292) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.677 (+/-0.287) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.693 (+/-0.303) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.566 (+/-0.245) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.585 (+/-0.229) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.586 (+/-0.227) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.007) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.003) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.642 (+/-0.236) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.681 (+/-0.255) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.681 (+/-0.293) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.693 (+/-0.298) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.677 (+/-0.297) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.690 (+/-0.311) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.590 (+/-0.261) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.585 (+/-0.230) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.586 (+/-0.227) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.007) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.003) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.682 (+/-0.293) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.658 (+/-0.243) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.680 (+/-0.290) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.692 (+/-0.297) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.693 (+/-0.314) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.688 (+/-0.312) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.589 (+/-0.260) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.585 (+/-0.230) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.586 (+/-0.227) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.007) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.003) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'linear'}
0.616 (+/-0.237) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.658 (+/-0.216) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.657 (+/-0.215) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.698 (+/-0.305) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.681 (+/-0.293) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.681 (+/-0.293) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.681 (+/-0.293) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.656 (+/-0.214) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.656 (+/-0.213) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.639 (+/-0.233) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 0.6309573444801931, 'kernel': 'linear'}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6977543490807157
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.618 (+/-0.214) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.630 (+/-0.192) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.327) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.618 (+/-0.172) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.623 (+/-0.183) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.631 (+/-0.395) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.618 (+/-0.172) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.167) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.581 (+/-0.180) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.549 (+/-0.155) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.625 (+/-0.179) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.610 (+/-0.163) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.582 (+/-0.180) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.568 (+/-0.175) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.550 (+/-0.157) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.618 (+/-0.172) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.614 (+/-0.167) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.582 (+/-0.180) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.579 (+/-0.178) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.555 (+/-0.199) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.547 (+/-0.154) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.747 (+/-0.502) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.622 (+/-0.174) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.614 (+/-0.169) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.582 (+/-0.180) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.570 (+/-0.152) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.571 (+/-0.191) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.561 (+/-0.195) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.547 (+/-0.154) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.597 (+/-0.177) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.610 (+/-0.166) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.583 (+/-0.182) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.567 (+/-0.144) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.569 (+/-0.148) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.547 (+/-0.171) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.561 (+/-0.195) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.547 (+/-0.154) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.798 (+/-0.437) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.530 (+/-0.146) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.586 (+/-0.155) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.561 (+/-0.146) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.571 (+/-0.147) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.584 (+/-0.176) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.553 (+/-0.167) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.561 (+/-0.195) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.547 (+/-0.154) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.775 (+/-0.457) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.550 (+/-0.298) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.578 (+/-0.149) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.558 (+/-0.144) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.592 (+/-0.189) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.586 (+/-0.177) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.552 (+/-0.167) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.561 (+/-0.195) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.547 (+/-0.154) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.764 (+/-0.421) for {'C': 0.1, 'kernel': 'linear'}
0.614 (+/-0.179) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.630 (+/-0.180) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.627 (+/-0.185) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.630 (+/-0.189) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.630 (+/-0.189) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.619 (+/-0.169) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.610 (+/-0.163) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.610 (+/-0.163) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.616 (+/-0.182) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.603 (+/-0.179) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [1]
检查交叉验证集中测试集标签是否有预测不到的值: {-1}
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 1.00 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.640 (+/-0.234) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.306) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.006) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.497 (+/-0.006) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.681 (+/-0.293) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.214) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.611 (+/-0.234) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.494 (+/-0.009) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.003) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.681 (+/-0.293) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.681 (+/-0.292) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.652 (+/-0.207) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.583 (+/-0.232) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.007) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.003) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.306) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.680 (+/-0.292) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.676 (+/-0.286) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.609 (+/-0.241) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.585 (+/-0.230) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.007) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.003) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.681 (+/-0.293) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.681 (+/-0.292) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.676 (+/-0.286) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.675 (+/-0.290) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.555 (+/-0.218) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.586 (+/-0.227) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.007) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.003) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.617 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.681 (+/-0.293) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.681 (+/-0.293) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.676 (+/-0.286) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.674 (+/-0.290) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.596 (+/-0.236) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.585 (+/-0.230) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.586 (+/-0.227) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.007) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.003) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.679 (+/-0.293) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.697 (+/-0.303) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.692 (+/-0.298) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.672 (+/-0.294) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.684 (+/-0.313) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.564 (+/-0.249) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.585 (+/-0.229) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.586 (+/-0.227) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.007) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.003) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.658 (+/-0.216) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.581 (+/-0.278) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.694 (+/-0.298) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.687 (+/-0.287) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.667 (+/-0.305) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.682 (+/-0.294) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.590 (+/-0.261) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.585 (+/-0.230) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.586 (+/-0.227) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.007) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.003) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.682 (+/-0.293) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.537 (+/-0.183) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.692 (+/-0.292) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.686 (+/-0.286) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.683 (+/-0.315) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.682 (+/-0.295) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.589 (+/-0.260) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.585 (+/-0.230) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.586 (+/-0.227) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.007) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.003) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.658 (+/-0.216) for {'C': 0.1, 'kernel': 'linear'}
0.656 (+/-0.213) for {'C': 0.15848931924611137, 'kernel': 'linear'}
0.698 (+/-0.306) for {'C': 0.251188643150958, 'kernel': 'linear'}
0.697 (+/-0.305) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.698 (+/-0.306) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.698 (+/-0.306) for {'C': 0.9999999999999997, 'kernel': 'linear'}
0.681 (+/-0.292) for {'C': 1.5848931924611132, 'kernel': 'linear'}
0.680 (+/-0.292) for {'C': 2.5118864315095797, 'kernel': 'linear'}
0.680 (+/-0.292) for {'C': 3.9810717055349714, 'kernel': 'linear'}
0.680 (+/-0.292) for {'C': 6.309573444801929, 'kernel': 'linear'}
0.679 (+/-0.291) for {'C': 9.999999999999998, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 0.6309573444801931, 'kernel': 'linear'}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.697594607964383
循环迭代之前,delta is: [0.36904266]
执行tunemodel()函数前,使用的grid search parameter是:
[{'C': array([1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02, 1.e+03, 1.e+04, 1.e+05,
1.e+06, 1.e+07, 1.e+08]), 'kernel': ['rbf'], 'gamma': array([1.e-08, 1.e-07, 1.e-06, 1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01,
1.e+00, 1.e+01, 1.e+02])}, {'C': array([0.39810717, 0.43651583, 0.47863009, 0.52480746, 0.57543994,
0.63095734, 0.69183097, 0.75857758, 0.83176377, 0.91201084,
1. ]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.327) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.689 (+/-0.436) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.635 (+/-0.198) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.689 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.689 (+/-0.436) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.630 (+/-0.189) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.635 (+/-0.326) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.564 (+/-0.195) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.689 (+/-0.436) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.630 (+/-0.189) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.603 (+/-0.195) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.609 (+/-0.322) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.550 (+/-0.157) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.689 (+/-0.436) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.630 (+/-0.189) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.599 (+/-0.192) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.577 (+/-0.153) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.580 (+/-0.225) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.547 (+/-0.154) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.689 (+/-0.436) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.643 (+/-0.202) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.603 (+/-0.195) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.573 (+/-0.152) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.621 (+/-0.312) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.561 (+/-0.195) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.547 (+/-0.154) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.639 (+/-0.285) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.634 (+/-0.190) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.620 (+/-0.184) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.577 (+/-0.155) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.594 (+/-0.178) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.547 (+/-0.171) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.561 (+/-0.195) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.547 (+/-0.154) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.797 (+/-0.492) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.624 (+/-0.212) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.623 (+/-0.184) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.580 (+/-0.155) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.595 (+/-0.177) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.592 (+/-0.168) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.553 (+/-0.167) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.561 (+/-0.195) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.547 (+/-0.154) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.779 (+/-0.452) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.648 (+/-0.320) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.614 (+/-0.182) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.577 (+/-0.154) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.618 (+/-0.201) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.592 (+/-0.172) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.552 (+/-0.167) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.561 (+/-0.195) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.547 (+/-0.154) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.648 (+/-0.209) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.648 (+/-0.209) for {'C': 0.43651583224016594, 'kernel': 'linear'}
0.639 (+/-0.202) for {'C': 0.47863009232263826, 'kernel': 'linear'}
0.635 (+/-0.198) for {'C': 0.5248074602497725, 'kernel': 'linear'}
0.635 (+/-0.198) for {'C': 0.5754399373371568, 'kernel': 'linear'}
0.643 (+/-0.202) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.646 (+/-0.204) for {'C': 0.6918309709189364, 'kernel': 'linear'}
0.660 (+/-0.219) for {'C': 0.7585775750291837, 'kernel': 'linear'}
0.643 (+/-0.202) for {'C': 0.8317637711026709, 'kernel': 'linear'}
0.643 (+/-0.202) for {'C': 0.9120108393559095, 'kernel': 'linear'}
0.643 (+/-0.202) for {'C': 0.9999999999999997, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [1]
检查交叉验证集中测试集标签是否有预测不到的值: {-1}
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 1.00 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.002) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.497 (+/-0.006) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.214) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.004) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.003) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.616 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.681 (+/-0.293) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.589 (+/-0.226) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.007) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.003) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.681 (+/-0.293) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.639 (+/-0.233) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.586 (+/-0.229) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.007) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.003) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.616 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.681 (+/-0.293) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.639 (+/-0.233) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.677 (+/-0.288) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.587 (+/-0.232) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.586 (+/-0.227) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.007) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.003) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.616 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.681 (+/-0.293) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.639 (+/-0.233) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.677 (+/-0.287) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.628 (+/-0.227) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.585 (+/-0.230) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.586 (+/-0.227) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.007) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.003) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.681 (+/-0.294) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.681 (+/-0.293) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.681 (+/-0.292) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.677 (+/-0.287) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.693 (+/-0.303) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.566 (+/-0.245) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.585 (+/-0.229) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.586 (+/-0.227) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.007) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.003) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.642 (+/-0.236) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.681 (+/-0.255) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.681 (+/-0.293) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.693 (+/-0.298) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.677 (+/-0.297) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.690 (+/-0.311) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.590 (+/-0.261) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.585 (+/-0.230) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.586 (+/-0.227) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.007) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.003) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.682 (+/-0.293) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.658 (+/-0.243) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.680 (+/-0.290) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.692 (+/-0.297) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.693 (+/-0.314) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.688 (+/-0.312) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.589 (+/-0.260) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.585 (+/-0.230) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.586 (+/-0.227) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.007) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.003) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.657 (+/-0.215) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.682 (+/-0.294) for {'C': 0.43651583224016594, 'kernel': 'linear'}
0.681 (+/-0.294) for {'C': 0.47863009232263826, 'kernel': 'linear'}
0.681 (+/-0.293) for {'C': 0.5248074602497725, 'kernel': 'linear'}
0.681 (+/-0.293) for {'C': 0.5754399373371568, 'kernel': 'linear'}
0.698 (+/-0.305) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.698 (+/-0.306) for {'C': 0.6918309709189364, 'kernel': 'linear'}
0.698 (+/-0.306) for {'C': 0.7585775750291837, 'kernel': 'linear'}
0.681 (+/-0.293) for {'C': 0.8317637711026709, 'kernel': 'linear'}
0.681 (+/-0.293) for {'C': 0.9120108393559095, 'kernel': 'linear'}
0.681 (+/-0.293) for {'C': 0.9999999999999997, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 0.7585775750291837, 'kernel': 'linear'}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6982356336054085
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.618 (+/-0.214) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.630 (+/-0.192) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.327) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.618 (+/-0.172) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.623 (+/-0.183) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.631 (+/-0.395) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.618 (+/-0.172) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.167) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.581 (+/-0.180) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.549 (+/-0.155) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.625 (+/-0.179) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.610 (+/-0.163) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.582 (+/-0.180) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.568 (+/-0.175) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.550 (+/-0.157) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.618 (+/-0.172) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.614 (+/-0.167) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.582 (+/-0.180) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.579 (+/-0.178) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.555 (+/-0.199) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.547 (+/-0.154) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.747 (+/-0.502) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.622 (+/-0.174) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.614 (+/-0.169) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.582 (+/-0.180) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.570 (+/-0.152) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.571 (+/-0.191) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.561 (+/-0.195) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.547 (+/-0.154) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.597 (+/-0.177) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.610 (+/-0.166) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.583 (+/-0.182) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.567 (+/-0.144) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.569 (+/-0.148) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.547 (+/-0.171) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.561 (+/-0.195) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.547 (+/-0.154) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.798 (+/-0.437) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.530 (+/-0.146) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.586 (+/-0.155) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.561 (+/-0.146) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.571 (+/-0.147) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.584 (+/-0.176) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.553 (+/-0.167) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.561 (+/-0.195) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.547 (+/-0.154) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.775 (+/-0.457) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.550 (+/-0.298) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.578 (+/-0.149) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.558 (+/-0.144) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.592 (+/-0.189) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.586 (+/-0.177) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.552 (+/-0.167) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.561 (+/-0.195) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.547 (+/-0.154) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.627 (+/-0.185) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.630 (+/-0.189) for {'C': 0.43651583224016594, 'kernel': 'linear'}
0.630 (+/-0.189) for {'C': 0.47863009232263826, 'kernel': 'linear'}
0.630 (+/-0.189) for {'C': 0.5248074602497725, 'kernel': 'linear'}
0.630 (+/-0.189) for {'C': 0.5754399373371568, 'kernel': 'linear'}
0.630 (+/-0.189) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.630 (+/-0.189) for {'C': 0.6918309709189364, 'kernel': 'linear'}
0.630 (+/-0.189) for {'C': 0.7585775750291837, 'kernel': 'linear'}
0.630 (+/-0.189) for {'C': 0.8317637711026709, 'kernel': 'linear'}
0.630 (+/-0.189) for {'C': 0.9120108393559095, 'kernel': 'linear'}
0.630 (+/-0.189) for {'C': 0.9999999999999997, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [1]
检查交叉验证集中测试集标签是否有预测不到的值: {-1}
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 1.00 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.640 (+/-0.234) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.306) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.006) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.497 (+/-0.006) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.681 (+/-0.293) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.214) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.611 (+/-0.234) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.494 (+/-0.009) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.003) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.681 (+/-0.293) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.681 (+/-0.292) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.652 (+/-0.207) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.583 (+/-0.232) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.007) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.003) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.306) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.680 (+/-0.292) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.676 (+/-0.286) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.609 (+/-0.241) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.585 (+/-0.230) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.007) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.003) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.681 (+/-0.293) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.681 (+/-0.292) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.676 (+/-0.286) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.675 (+/-0.290) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.555 (+/-0.218) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.586 (+/-0.227) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.007) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.003) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.617 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.681 (+/-0.293) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.681 (+/-0.293) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.676 (+/-0.286) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.674 (+/-0.290) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.596 (+/-0.236) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.585 (+/-0.230) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.586 (+/-0.227) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.007) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.003) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.679 (+/-0.293) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.697 (+/-0.303) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.692 (+/-0.298) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.672 (+/-0.294) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.684 (+/-0.313) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.564 (+/-0.249) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.585 (+/-0.229) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.586 (+/-0.227) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.007) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.003) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.658 (+/-0.216) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.581 (+/-0.278) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.694 (+/-0.298) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.687 (+/-0.287) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.667 (+/-0.305) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.682 (+/-0.294) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.590 (+/-0.261) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.585 (+/-0.230) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.586 (+/-0.227) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.007) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.003) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.682 (+/-0.293) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.537 (+/-0.183) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.692 (+/-0.292) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.686 (+/-0.286) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.683 (+/-0.315) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.682 (+/-0.295) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.589 (+/-0.260) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.585 (+/-0.230) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.586 (+/-0.227) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.007) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.003) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.697 (+/-0.305) for {'C': 0.39810717055349726, 'kernel': 'linear'}
0.698 (+/-0.306) for {'C': 0.43651583224016594, 'kernel': 'linear'}
0.698 (+/-0.306) for {'C': 0.47863009232263826, 'kernel': 'linear'}
0.698 (+/-0.306) for {'C': 0.5248074602497725, 'kernel': 'linear'}
0.698 (+/-0.306) for {'C': 0.5754399373371568, 'kernel': 'linear'}
0.698 (+/-0.306) for {'C': 0.6309573444801931, 'kernel': 'linear'}
0.698 (+/-0.306) for {'C': 0.6918309709189364, 'kernel': 'linear'}
0.698 (+/-0.306) for {'C': 0.7585775750291837, 'kernel': 'linear'}
0.698 (+/-0.306) for {'C': 0.8317637711026709, 'kernel': 'linear'}
0.698 (+/-0.306) for {'C': 0.9120108393559095, 'kernel': 'linear'}
0.698 (+/-0.306) for {'C': 0.9999999999999997, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 0.43651583224016594, 'kernel': 'linear'}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.697594607964383
第0轮loop中,tunemodel函数得到的最优参数是: ([{'C': array([1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02, 1.e+03, 1.e+04, 1.e+05,
1.e+06, 1.e+07, 1.e+08]), 'kernel': ['rbf'], 'gamma': array([1.e-08, 1.e-07, 1.e-06, 1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01,
1.e+00, 1.e+01, 1.e+02])}, {'C': array([0.69183097, 0.70469307, 0.71779429, 0.73113908, 0.74473197,
0.75857758, 0.77268059, 0.78704579, 0.80167806, 0.81658237,
0.83176377]), 'kernel': ['linear']}], {'C': 0.7585775750291837, 'kernel': 'linear'}, 0.6982356336054085)
这是第0次迭代微调C和gamma。
第0次迭代,得到delta: [0.12762023]
执行tunemodel()函数前,使用的grid search parameter是:
[{'C': array([1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02, 1.e+03, 1.e+04, 1.e+05,
1.e+06, 1.e+07, 1.e+08]), 'kernel': ['rbf'], 'gamma': array([1.e-08, 1.e-07, 1.e-06, 1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01,
1.e+00, 1.e+01, 1.e+02])}, {'C': array([0.69183097, 0.70469307, 0.71779429, 0.73113908, 0.74473197,
0.75857758, 0.77268059, 0.78704579, 0.80167806, 0.81658237,
0.83176377]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.437) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.327) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.689 (+/-0.436) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.635 (+/-0.198) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.689 (+/-0.437) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.689 (+/-0.436) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.630 (+/-0.189) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.635 (+/-0.326) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.564 (+/-0.195) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.689 (+/-0.436) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.630 (+/-0.189) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.603 (+/-0.195) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.609 (+/-0.322) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.550 (+/-0.157) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.689 (+/-0.436) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.630 (+/-0.189) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.599 (+/-0.192) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.577 (+/-0.153) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.580 (+/-0.225) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.547 (+/-0.154) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.689 (+/-0.436) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.643 (+/-0.202) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.603 (+/-0.195) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.573 (+/-0.152) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.621 (+/-0.312) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.561 (+/-0.195) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.547 (+/-0.154) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.639 (+/-0.285) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.634 (+/-0.190) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.620 (+/-0.184) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.577 (+/-0.155) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.594 (+/-0.178) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.547 (+/-0.171) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.561 (+/-0.195) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.547 (+/-0.154) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.797 (+/-0.492) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.624 (+/-0.212) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.623 (+/-0.184) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.580 (+/-0.155) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.595 (+/-0.177) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.592 (+/-0.168) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.553 (+/-0.167) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.561 (+/-0.195) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.547 (+/-0.154) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.779 (+/-0.452) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.648 (+/-0.320) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.614 (+/-0.182) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.577 (+/-0.154) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.618 (+/-0.201) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.592 (+/-0.172) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.552 (+/-0.167) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.561 (+/-0.195) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.547 (+/-0.154) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.646 (+/-0.204) for {'C': 0.6918309709189364, 'kernel': 'linear'}
0.646 (+/-0.204) for {'C': 0.7046930689671468, 'kernel': 'linear'}
0.646 (+/-0.204) for {'C': 0.7177942912713616, 'kernel': 'linear'}
0.643 (+/-0.202) for {'C': 0.7311390834834173, 'kernel': 'linear'}
0.651 (+/-0.211) for {'C': 0.7447319739059889, 'kernel': 'linear'}
0.660 (+/-0.219) for {'C': 0.7585775750291837, 'kernel': 'linear'}
0.643 (+/-0.202) for {'C': 0.7726805850957021, 'kernel': 'linear'}
0.643 (+/-0.202) for {'C': 0.7870457896950985, 'kernel': 'linear'}
0.643 (+/-0.202) for {'C': 0.801678063387679, 'kernel': 'linear'}
0.643 (+/-0.202) for {'C': 0.8165823713585922, 'kernel': 'linear'}
0.643 (+/-0.202) for {'C': 0.8317637711026709, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [1]
检查交叉验证集中测试集标签是否有预测不到的值: {-1}
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 1.00 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.617 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.002) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.497 (+/-0.006) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.616 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.214) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.004) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.003) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.616 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.681 (+/-0.293) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.589 (+/-0.226) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.007) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.003) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.616 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.681 (+/-0.293) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.639 (+/-0.233) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.612 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.586 (+/-0.229) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.007) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.003) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.616 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.681 (+/-0.293) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.639 (+/-0.233) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.677 (+/-0.288) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.587 (+/-0.232) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.586 (+/-0.227) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.007) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.003) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.616 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.681 (+/-0.293) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.639 (+/-0.233) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.677 (+/-0.287) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.628 (+/-0.227) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.585 (+/-0.230) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.586 (+/-0.227) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.007) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.003) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.681 (+/-0.294) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.681 (+/-0.293) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.681 (+/-0.292) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.677 (+/-0.287) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.693 (+/-0.303) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.566 (+/-0.245) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.585 (+/-0.229) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.586 (+/-0.227) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.007) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.003) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.642 (+/-0.236) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.681 (+/-0.255) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.681 (+/-0.293) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.693 (+/-0.298) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.677 (+/-0.297) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.690 (+/-0.311) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.590 (+/-0.261) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.585 (+/-0.230) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.586 (+/-0.227) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.007) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.003) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.682 (+/-0.293) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.658 (+/-0.243) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.680 (+/-0.290) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.692 (+/-0.297) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.693 (+/-0.314) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.688 (+/-0.312) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.589 (+/-0.260) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.585 (+/-0.230) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.586 (+/-0.227) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.007) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.003) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.698 (+/-0.306) for {'C': 0.6918309709189364, 'kernel': 'linear'}
0.698 (+/-0.306) for {'C': 0.7046930689671468, 'kernel': 'linear'}
0.698 (+/-0.306) for {'C': 0.7177942912713616, 'kernel': 'linear'}
0.681 (+/-0.293) for {'C': 0.7311390834834173, 'kernel': 'linear'}
0.682 (+/-0.294) for {'C': 0.7447319739059889, 'kernel': 'linear'}
0.698 (+/-0.306) for {'C': 0.7585775750291837, 'kernel': 'linear'}
0.681 (+/-0.293) for {'C': 0.7726805850957021, 'kernel': 'linear'}
0.681 (+/-0.293) for {'C': 0.7870457896950985, 'kernel': 'linear'}
0.681 (+/-0.293) for {'C': 0.801678063387679, 'kernel': 'linear'}
0.681 (+/-0.293) for {'C': 0.8165823713585922, 'kernel': 'linear'}
0.681 (+/-0.293) for {'C': 0.8317637711026709, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 0.7585775750291837, 'kernel': 'linear'}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6982356336054085
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.618 (+/-0.214) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.630 (+/-0.192) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.639 (+/-0.327) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.618 (+/-0.172) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.623 (+/-0.183) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.631 (+/-0.395) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.618 (+/-0.172) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.614 (+/-0.167) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.581 (+/-0.180) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.549 (+/-0.155) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.625 (+/-0.179) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.610 (+/-0.163) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.582 (+/-0.180) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.568 (+/-0.175) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.550 (+/-0.157) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.618 (+/-0.172) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.614 (+/-0.167) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.582 (+/-0.180) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.579 (+/-0.178) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.555 (+/-0.199) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.547 (+/-0.154) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.747 (+/-0.502) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.622 (+/-0.174) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.614 (+/-0.169) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.582 (+/-0.180) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.570 (+/-0.152) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.571 (+/-0.191) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.561 (+/-0.195) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.547 (+/-0.154) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.597 (+/-0.177) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.610 (+/-0.166) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.583 (+/-0.182) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.567 (+/-0.144) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.569 (+/-0.148) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.547 (+/-0.171) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.561 (+/-0.195) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.547 (+/-0.154) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.798 (+/-0.437) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.530 (+/-0.146) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.586 (+/-0.155) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.561 (+/-0.146) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.571 (+/-0.147) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.584 (+/-0.176) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.553 (+/-0.167) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.561 (+/-0.195) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.547 (+/-0.154) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.775 (+/-0.457) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.550 (+/-0.298) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.578 (+/-0.149) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.558 (+/-0.144) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.592 (+/-0.189) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.586 (+/-0.177) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.552 (+/-0.167) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.561 (+/-0.195) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.547 (+/-0.154) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.630 (+/-0.189) for {'C': 0.6918309709189364, 'kernel': 'linear'}
0.630 (+/-0.189) for {'C': 0.7046930689671468, 'kernel': 'linear'}
0.630 (+/-0.189) for {'C': 0.7177942912713616, 'kernel': 'linear'}
0.630 (+/-0.189) for {'C': 0.7311390834834173, 'kernel': 'linear'}
0.630 (+/-0.189) for {'C': 0.7447319739059889, 'kernel': 'linear'}
0.630 (+/-0.189) for {'C': 0.7585775750291837, 'kernel': 'linear'}
0.630 (+/-0.189) for {'C': 0.7726805850957021, 'kernel': 'linear'}
0.630 (+/-0.189) for {'C': 0.7870457896950985, 'kernel': 'linear'}
0.630 (+/-0.189) for {'C': 0.801678063387679, 'kernel': 'linear'}
0.630 (+/-0.189) for {'C': 0.8165823713585922, 'kernel': 'linear'}
0.630 (+/-0.189) for {'C': 0.8317637711026709, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [1]
检查交叉验证集中测试集标签是否有预测不到的值: {-1}
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 1.00 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.640 (+/-0.234) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.306) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.615 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.006) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.497 (+/-0.006) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.681 (+/-0.293) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.214) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.611 (+/-0.234) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.494 (+/-0.009) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.003) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.681 (+/-0.293) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.681 (+/-0.292) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.652 (+/-0.207) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.583 (+/-0.232) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.007) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.003) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.306) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.680 (+/-0.292) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.676 (+/-0.286) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.609 (+/-0.241) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.585 (+/-0.230) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.007) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.003) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.681 (+/-0.293) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.681 (+/-0.292) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.676 (+/-0.286) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.675 (+/-0.290) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.555 (+/-0.218) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.586 (+/-0.227) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.007) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.003) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.617 (+/-0.238) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.681 (+/-0.293) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.681 (+/-0.293) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.676 (+/-0.286) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.674 (+/-0.290) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.596 (+/-0.236) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.585 (+/-0.230) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.586 (+/-0.227) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.007) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.003) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.679 (+/-0.293) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.697 (+/-0.303) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.692 (+/-0.298) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.672 (+/-0.294) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.684 (+/-0.313) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.564 (+/-0.249) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.585 (+/-0.229) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.586 (+/-0.227) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.007) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.003) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.658 (+/-0.216) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.581 (+/-0.278) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.694 (+/-0.298) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.687 (+/-0.287) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.667 (+/-0.305) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.682 (+/-0.294) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.590 (+/-0.261) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.585 (+/-0.230) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.586 (+/-0.227) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.007) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.003) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.682 (+/-0.293) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.537 (+/-0.183) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.692 (+/-0.292) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.686 (+/-0.286) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.683 (+/-0.315) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.682 (+/-0.295) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.589 (+/-0.260) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.585 (+/-0.230) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.586 (+/-0.227) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.007) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.003) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.698 (+/-0.306) for {'C': 0.6918309709189364, 'kernel': 'linear'}
0.698 (+/-0.306) for {'C': 0.7046930689671468, 'kernel': 'linear'}
0.698 (+/-0.306) for {'C': 0.7177942912713616, 'kernel': 'linear'}
0.698 (+/-0.306) for {'C': 0.7311390834834173, 'kernel': 'linear'}
0.698 (+/-0.306) for {'C': 0.7447319739059889, 'kernel': 'linear'}
0.698 (+/-0.306) for {'C': 0.7585775750291837, 'kernel': 'linear'}
0.698 (+/-0.306) for {'C': 0.7726805850957021, 'kernel': 'linear'}
0.698 (+/-0.306) for {'C': 0.7870457896950985, 'kernel': 'linear'}
0.698 (+/-0.306) for {'C': 0.801678063387679, 'kernel': 'linear'}
0.698 (+/-0.306) for {'C': 0.8165823713585922, 'kernel': 'linear'}
0.698 (+/-0.306) for {'C': 0.8317637711026709, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 0.6918309709189364, 'kernel': 'linear'}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.697594607964383
第1轮loop中,tunemodel函数得到的最优参数是: ([{'C': array([1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02, 1.e+03, 1.e+04, 1.e+05,
1.e+06, 1.e+07, 1.e+08]), 'kernel': ['rbf'], 'gamma': array([1.e-08, 1.e-07, 1.e-06, 1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01,
1.e+00, 1.e+01, 1.e+02])}, {'C': array([0.74473197, 0.74748073, 0.75023963, 0.75300871, 0.75578801,
0.75857758, 0.76137743, 0.76418762, 0.76700819, 0.76983916,
0.77268059]), 'kernel': ['linear']}], {'C': 0.7585775750291837, 'kernel': 'linear'}, 0.6982356336054085)
这是第1次迭代微调C和gamma。
第1次迭代,得到delta: [0.]
SVC模型clf_op_iter的参数是: {'kernel': 'linear', 'decision_function_shape': 'ovr', 'class_weight': {-1: 30}, 'tol': 0.001, 'gamma': 'auto', 'random_state': 0, 'cache_size': 200, 'verbose': False, 'degree': 3, 'C': 0.7585775750291837, 'probability': False, 'shrinking': True, 'coef0': 0.0, 'max_iter': -1}
训练模型SVC对训练样本的预测准确率: 0.8851522842639594
测试集中,预测为舞弊样本的有: (array([ 10, 12, 22, 24, 38, 56, 65, 67, 112, 114, 115,
117, 121, 136, 143, 144, 145, 146, 180, 181, 182, 183,
208, 214, 216, 236, 243, 247, 255, 281, 286, 287, 288,
300, 330, 349, 364, 366, 370, 403, 404, 433, 442, 449,
457, 460, 468, 471, 472, 473, 474, 475, 476, 477, 481,
482, 483, 484, 485, 487, 488, 489, 490, 509, 516, 535,
543, 544, 545, 549, 562, 563, 565, 566, 567, 587, 590,
611, 629, 630, 635, 641, 661, 667, 687, 695, 696, 697,
718, 720, 721, 722, 723, 736, 742, 743, 750, 752, 775,
776, 777, 778, 787, 790, 820, 845, 860, 862, 874, 879,
880, 882, 883, 884, 885, 897, 898, 902, 929, 930, 931,
960, 961, 963, 964, 966, 967, 998, 1013, 1014, 1015, 1016,
1017, 1037, 1049, 1050, 1051, 1067, 1089, 1112, 1113, 1114, 1122,
1124, 1129, 1141, 1142, 1148, 1154, 1156, 1158, 1160, 1164, 1180,
1183, 1194, 1200, 1222, 1230, 1241, 1242, 1243, 1246, 1247, 1248,
1249, 1250, 1251, 1252, 1254, 1255, 1256], dtype=int64),)
测试集中,实际为舞弊样本的有: (array([1246, 1247, 1248, 1249, 1250, 1251, 1252, 1253, 1254, 1255, 1256],
dtype=int64),)
预测的舞弊样本数目: 172
训练模型SVC对测试样本的预测准确率: 0.8781725888324873
以上是第45次特征筛选。
第45次特征筛选,AUC值是: 0.8895374288632715
X_train_iter_svc.shape is: (1257, 7)
X_test_iter_svc.shape is: (1257, 7)
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.581 (+/-0.180) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.526 (+/-0.120) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.630 (+/-0.189) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.25 0.17 0.20 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.614 (+/-0.236) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.498 (+/-0.004) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.548 (+/-0.198) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.697 (+/-0.308) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.499 (+/-0.004) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.25 0.17 0.20 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.99 629
本轮grid search结果,得到最好的参数选择是: {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6967933259131008
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.625 (+/-0.179) for {'C': 1.0, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.578 (+/-0.145) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.25 0.17 0.20 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.99 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.697 (+/-0.308) for {'C': 1.0, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.693 (+/-0.302) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.25 0.17 0.20 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.99 629
本轮grid search结果,得到最好的参数选择是: {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6966325542089208
RBF的粗grid search最佳超参: {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001} RBF的粗grid search最高分值: 0.6967933259131008
linear的粗grid search最佳超参: {'C': 1.0, 'kernel': 'linear'} linear的粗grid search最高分值: 0.6966325542089208
粗grid search得到的parameter是:
[{'C': array([1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02, 1.e+03, 1.e+04, 1.e+05,
1.e+06, 1.e+07, 1.e+08]), 'kernel': ['rbf'], 'gamma': array([1.e-08, 1.e-07, 1.e-06, 1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01,
1.e+00, 1.e+01, 1.e+02])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.689 (+/-0.436) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.639 (+/-0.326) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.581 (+/-0.180) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.639 (+/-0.326) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.630 (+/-0.189) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.585 (+/-0.186) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.505 (+/-0.051) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.639 (+/-0.326) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.630 (+/-0.189) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.284) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.546 (+/-0.170) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.505 (+/-0.051) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.639 (+/-0.326) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.630 (+/-0.189) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.655 (+/-0.280) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.624 (+/-0.286) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.526 (+/-0.120) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.505 (+/-0.051) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.639 (+/-0.326) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.630 (+/-0.189) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.651 (+/-0.279) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.282) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.561 (+/-0.173) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.509 (+/-0.076) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.505 (+/-0.051) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.630 (+/-0.189) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.655 (+/-0.280) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.611 (+/-0.283) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.601 (+/-0.180) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.560 (+/-0.173) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.509 (+/-0.076) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.505 (+/-0.051) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.666 (+/-0.275) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.630 (+/-0.189) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.647 (+/-0.279) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.607 (+/-0.283) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.602 (+/-0.182) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.618 (+/-0.238) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.547 (+/-0.171) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.509 (+/-0.076) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.505 (+/-0.051) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.722 (+/-0.473) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.589 (+/-0.194) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.646 (+/-0.287) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.634 (+/-0.372) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.633 (+/-0.295) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.634 (+/-0.212) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.606 (+/-0.249) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.547 (+/-0.171) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.509 (+/-0.076) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.505 (+/-0.051) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.721 (+/-0.328) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.572 (+/-0.190) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.627 (+/-0.280) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.607 (+/-0.297) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.665 (+/-0.356) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.634 (+/-0.212) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.606 (+/-0.249) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.547 (+/-0.171) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.509 (+/-0.076) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.505 (+/-0.051) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.630 (+/-0.189) for {'C': 1.0, 'kernel': 'linear'}
0.655 (+/-0.280) for {'C': 10.0, 'kernel': 'linear'}
0.645 (+/-0.280) for {'C': 100.0, 'kernel': 'linear'}
0.582 (+/-0.153) for {'C': 1000.0, 'kernel': 'linear'}
0.619 (+/-0.185) for {'C': 10000.0, 'kernel': 'linear'}
0.641 (+/-0.286) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.003) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.002) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.236) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.003) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.002) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.308) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.236) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.523 (+/-0.146) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.497 (+/-0.008) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.308) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.217) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.563 (+/-0.206) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.523 (+/-0.146) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.498 (+/-0.007) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.615 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.308) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.308) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.695 (+/-0.303) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.548 (+/-0.198) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.521 (+/-0.148) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.497 (+/-0.006) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.308) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.307) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.694 (+/-0.303) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.580 (+/-0.209) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.522 (+/-0.149) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.521 (+/-0.148) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.497 (+/-0.006) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.697 (+/-0.308) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.308) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.694 (+/-0.301) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.695 (+/-0.302) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.589 (+/-0.226) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.519 (+/-0.151) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.521 (+/-0.148) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.497 (+/-0.006) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.680 (+/-0.193) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.697 (+/-0.308) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.697 (+/-0.307) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.693 (+/-0.300) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.694 (+/-0.304) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.647 (+/-0.255) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.562 (+/-0.208) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.520 (+/-0.151) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.521 (+/-0.148) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.497 (+/-0.006) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.616 (+/-0.239) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.662 (+/-0.228) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.697 (+/-0.303) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.691 (+/-0.293) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.696 (+/-0.305) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.696 (+/-0.306) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.620 (+/-0.265) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.558 (+/-0.214) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.519 (+/-0.151) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.521 (+/-0.148) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.497 (+/-0.006) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.706 (+/-0.269) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.633 (+/-0.241) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.696 (+/-0.301) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.691 (+/-0.292) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.304) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.696 (+/-0.305) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.617 (+/-0.271) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.557 (+/-0.215) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.519 (+/-0.151) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.521 (+/-0.148) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.497 (+/-0.006) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.616 (+/-0.239) for {'C': 0.1, 'kernel': 'linear'}
0.697 (+/-0.308) for {'C': 1.0, 'kernel': 'linear'}
0.697 (+/-0.308) for {'C': 10.0, 'kernel': 'linear'}
0.697 (+/-0.307) for {'C': 100.0, 'kernel': 'linear'}
0.693 (+/-0.302) for {'C': 1000.0, 'kernel': 'linear'}
0.696 (+/-0.307) for {'C': 10000.0, 'kernel': 'linear'}
0.696 (+/-0.307) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.7059284565916398
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.606 (+/-0.146) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.625 (+/-0.179) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.656 (+/-0.291) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.625 (+/-0.179) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.621 (+/-0.177) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.580 (+/-0.172) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.505 (+/-0.051) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.625 (+/-0.179) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.625 (+/-0.179) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.626 (+/-0.287) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.546 (+/-0.170) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.505 (+/-0.051) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.625 (+/-0.179) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.625 (+/-0.179) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.618 (+/-0.282) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.606 (+/-0.294) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.518 (+/-0.087) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.505 (+/-0.051) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.625 (+/-0.179) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.625 (+/-0.179) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.282) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.603 (+/-0.283) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.565 (+/-0.181) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.509 (+/-0.076) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.505 (+/-0.051) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.625 (+/-0.179) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.625 (+/-0.179) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.616 (+/-0.282) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.602 (+/-0.283) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.604 (+/-0.182) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.547 (+/-0.171) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.509 (+/-0.076) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.505 (+/-0.051) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.600 (+/-0.112) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.616 (+/-0.170) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.603 (+/-0.300) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.613 (+/-0.294) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.592 (+/-0.160) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.606 (+/-0.249) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.547 (+/-0.171) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.509 (+/-0.076) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.505 (+/-0.051) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.689 (+/-0.299) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.529 (+/-0.145) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.602 (+/-0.281) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.595 (+/-0.303) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.585 (+/-0.152) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.610 (+/-0.205) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.606 (+/-0.249) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.547 (+/-0.171) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.509 (+/-0.076) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.505 (+/-0.051) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.666 (+/-0.295) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.527 (+/-0.147) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.596 (+/-0.282) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.594 (+/-0.303) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.653 (+/-0.361) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.608 (+/-0.208) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.606 (+/-0.249) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.547 (+/-0.171) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.509 (+/-0.076) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.505 (+/-0.051) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.727 (+/-0.397) for {'C': 0.1, 'kernel': 'linear'}
0.625 (+/-0.179) for {'C': 1.0, 'kernel': 'linear'}
0.633 (+/-0.278) for {'C': 10.0, 'kernel': 'linear'}
0.598 (+/-0.284) for {'C': 100.0, 'kernel': 'linear'}
0.578 (+/-0.145) for {'C': 1000.0, 'kernel': 'linear'}
0.608 (+/-0.180) for {'C': 10000.0, 'kernel': 'linear'}
0.634 (+/-0.287) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.655 (+/-0.215) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.004) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.002) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.307) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.656 (+/-0.216) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.497 (+/-0.005) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.497 (+/-0.010) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.307) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.696 (+/-0.308) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.627 (+/-0.223) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.523 (+/-0.146) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.497 (+/-0.008) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.307) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.308) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.695 (+/-0.306) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.563 (+/-0.206) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.515 (+/-0.152) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.498 (+/-0.007) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.307) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.308) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.694 (+/-0.304) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.670 (+/-0.322) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.546 (+/-0.197) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.521 (+/-0.148) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.497 (+/-0.006) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.697 (+/-0.307) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.308) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.693 (+/-0.303) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.693 (+/-0.300) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.581 (+/-0.209) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.513 (+/-0.156) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.521 (+/-0.148) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.497 (+/-0.006) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.697 (+/-0.307) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.697 (+/-0.308) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.694 (+/-0.303) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.692 (+/-0.299) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.695 (+/-0.303) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.552 (+/-0.224) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.519 (+/-0.151) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.521 (+/-0.148) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.497 (+/-0.006) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.760 (+/-0.153) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.696 (+/-0.306) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.690 (+/-0.293) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.693 (+/-0.300) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.693 (+/-0.305) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.618 (+/-0.269) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.554 (+/-0.219) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.520 (+/-0.151) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.521 (+/-0.148) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.497 (+/-0.006) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.657 (+/-0.216) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.576 (+/-0.168) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.692 (+/-0.293) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.687 (+/-0.287) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.694 (+/-0.303) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.690 (+/-0.313) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.607 (+/-0.290) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.558 (+/-0.214) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.519 (+/-0.151) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.521 (+/-0.148) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.497 (+/-0.006) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.681 (+/-0.293) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.526 (+/-0.114) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.690 (+/-0.287) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.687 (+/-0.289) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.693 (+/-0.308) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.687 (+/-0.315) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.607 (+/-0.290) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.557 (+/-0.215) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.519 (+/-0.151) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.521 (+/-0.148) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.497 (+/-0.006) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.657 (+/-0.216) for {'C': 0.1, 'kernel': 'linear'}
0.697 (+/-0.308) for {'C': 1.0, 'kernel': 'linear'}
0.696 (+/-0.306) for {'C': 10.0, 'kernel': 'linear'}
0.691 (+/-0.298) for {'C': 100.0, 'kernel': 'linear'}
0.693 (+/-0.302) for {'C': 1000.0, 'kernel': 'linear'}
0.695 (+/-0.304) for {'C': 10000.0, 'kernel': 'linear'}
0.695 (+/-0.305) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.7602533184928684
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.496 (+/-0.001) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.496 (+/-0.001) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.496 (+/-0.001) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.496 (+/-0.001) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.496 (+/-0.001) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.496 (+/-0.001) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.496 (+/-0.001) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.496 (+/-0.001) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.547 (+/-0.301) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.697 (+/-0.437) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.639 (+/-0.326) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.496 (+/-0.001) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.496 (+/-0.001) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.496 (+/-0.001) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.496 (+/-0.001) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.496 (+/-0.001) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.496 (+/-0.001) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.496 (+/-0.001) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.547 (+/-0.301) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.689 (+/-0.436) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.689 (+/-0.374) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.660 (+/-0.290) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.496 (+/-0.001) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.496 (+/-0.001) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.496 (+/-0.001) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.496 (+/-0.001) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.496 (+/-0.001) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.496 (+/-0.001) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.597 (+/-0.402) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.689 (+/-0.436) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.689 (+/-0.374) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.643 (+/-0.209) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.618 (+/-0.172) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.496 (+/-0.001) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.496 (+/-0.001) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.496 (+/-0.001) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.496 (+/-0.001) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.496 (+/-0.001) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.597 (+/-0.402) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.689 (+/-0.436) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.689 (+/-0.374) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.627 (+/-0.185) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.625 (+/-0.179) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.644 (+/-0.164) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.496 (+/-0.001) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.496 (+/-0.001) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.496 (+/-0.001) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.496 (+/-0.001) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.747 (+/-0.502) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.702 (+/-0.420) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.689 (+/-0.374) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.677 (+/-0.299) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.656 (+/-0.138) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.660 (+/-0.136) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.630 (+/-0.189) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.656 (+/-0.312) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.666 (+/-0.275) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.693 (+/-0.354) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.635 (+/-0.153) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.644 (+/-0.120) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.669 (+/-0.142) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.630 (+/-0.189) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.604 (+/-0.171) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.672 (+/-0.287) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.630 (+/-0.189) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.710 (+/-0.209) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.618 (+/-0.172) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.677 (+/-0.150) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.625 (+/-0.179) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.496 (+/-0.001) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.496 (+/-0.001) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.647 (+/-0.460) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.747 (+/-0.502) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.544 (+/-0.101) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.649 (+/-0.290) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.612 (+/-0.165) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.708 (+/-0.220) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.630 (+/-0.189) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.677 (+/-0.150) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.630 (+/-0.192) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.496 (+/-0.001) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.647 (+/-0.460) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.747 (+/-0.502) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.702 (+/-0.429) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.511 (+/-0.032) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.623 (+/-0.295) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.624 (+/-0.166) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.724 (+/-0.211) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.655 (+/-0.285) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.673 (+/-0.153) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.630 (+/-0.192) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.647 (+/-0.460) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.722 (+/-0.473) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.739 (+/-0.451) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.691 (+/-0.370) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.503 (+/-0.010) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.595 (+/-0.196) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.619 (+/-0.169) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.708 (+/-0.232) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.655 (+/-0.285) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.698 (+/-0.247) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.646 (+/-0.287) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.722 (+/-0.473) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.714 (+/-0.418) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.689 (+/-0.299) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.732 (+/-0.391) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.453 (+/-0.300) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.589 (+/-0.194) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.609 (+/-0.185) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.714 (+/-0.225) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.659 (+/-0.285) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.694 (+/-0.242) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.646 (+/-0.287) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.630 (+/-0.189) for {'C': 1.0, 'kernel': 'linear'}
0.655 (+/-0.280) for {'C': 10.0, 'kernel': 'linear'}
0.645 (+/-0.280) for {'C': 100.0, 'kernel': 'linear'}
0.582 (+/-0.153) for {'C': 1000.0, 'kernel': 'linear'}
0.619 (+/-0.185) for {'C': 10000.0, 'kernel': 'linear'}
0.641 (+/-0.286) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.500 (+/-0.000) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.500 (+/-0.000) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.500 (+/-0.000) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.500 (+/-0.000) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.500 (+/-0.000) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.500 (+/-0.000) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.500 (+/-0.000) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.500 (+/-0.000) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.525 (+/-0.150) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.616 (+/-0.238) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.615 (+/-0.237) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.500 (+/-0.000) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.500 (+/-0.000) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.500 (+/-0.000) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.500 (+/-0.000) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.500 (+/-0.000) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.500 (+/-0.000) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.500 (+/-0.000) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.525 (+/-0.150) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.616 (+/-0.238) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.640 (+/-0.236) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.656 (+/-0.215) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.500 (+/-0.000) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.500 (+/-0.000) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.500 (+/-0.000) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.500 (+/-0.000) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.500 (+/-0.000) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.500 (+/-0.000) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.550 (+/-0.200) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.616 (+/-0.238) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.640 (+/-0.236) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.656 (+/-0.216) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.680 (+/-0.294) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.500 (+/-0.000) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.500 (+/-0.000) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.500 (+/-0.000) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.500 (+/-0.000) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.500 (+/-0.000) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.550 (+/-0.200) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.616 (+/-0.238) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.640 (+/-0.236) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.656 (+/-0.215) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.697 (+/-0.307) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.722 (+/-0.277) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.500 (+/-0.000) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.500 (+/-0.000) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.500 (+/-0.000) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.500 (+/-0.000) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.617 (+/-0.238) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.641 (+/-0.235) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.640 (+/-0.236) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.656 (+/-0.216) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.730 (+/-0.231) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.747 (+/-0.233) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.697 (+/-0.308) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.500 (+/-0.002) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.640 (+/-0.235) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.680 (+/-0.193) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.656 (+/-0.216) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.697 (+/-0.211) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.730 (+/-0.230) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.747 (+/-0.234) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.697 (+/-0.308) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.500 (+/-0.002) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.677 (+/-0.283) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.721 (+/-0.278) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.681 (+/-0.294) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.771 (+/-0.163) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.680 (+/-0.294) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.747 (+/-0.234) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.697 (+/-0.308) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.500 (+/-0.000) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.500 (+/-0.000) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.575 (+/-0.229) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.616 (+/-0.239) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.658 (+/-0.229) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.720 (+/-0.276) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.696 (+/-0.306) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.771 (+/-0.165) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.697 (+/-0.307) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.747 (+/-0.234) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.697 (+/-0.307) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.500 (+/-0.000) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.575 (+/-0.229) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.616 (+/-0.239) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.659 (+/-0.215) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.597 (+/-0.244) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.694 (+/-0.303) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.720 (+/-0.274) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.772 (+/-0.166) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.681 (+/-0.294) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.747 (+/-0.234) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.697 (+/-0.307) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.575 (+/-0.229) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.616 (+/-0.239) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.641 (+/-0.237) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.685 (+/-0.198) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.572 (+/-0.213) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.679 (+/-0.253) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.720 (+/-0.272) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.771 (+/-0.164) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.681 (+/-0.295) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.747 (+/-0.234) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.697 (+/-0.305) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.616 (+/-0.239) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.640 (+/-0.237) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.657 (+/-0.216) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.666 (+/-0.193) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.539 (+/-0.179) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.662 (+/-0.228) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.694 (+/-0.301) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.771 (+/-0.163) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.681 (+/-0.295) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.747 (+/-0.234) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.697 (+/-0.303) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.616 (+/-0.239) for {'C': 0.1, 'kernel': 'linear'}
0.697 (+/-0.308) for {'C': 1.0, 'kernel': 'linear'}
0.697 (+/-0.308) for {'C': 10.0, 'kernel': 'linear'}
0.697 (+/-0.307) for {'C': 100.0, 'kernel': 'linear'}
0.693 (+/-0.302) for {'C': 1000.0, 'kernel': 'linear'}
0.696 (+/-0.307) for {'C': 10000.0, 'kernel': 'linear'}
0.696 (+/-0.307) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.7719535823233572
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.496 (+/-0.001) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.496 (+/-0.001) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.496 (+/-0.001) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.496 (+/-0.001) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.496 (+/-0.001) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.496 (+/-0.001) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.597 (+/-0.402) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.697 (+/-0.437) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.639 (+/-0.326) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.627 (+/-0.185) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.625 (+/-0.179) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.496 (+/-0.001) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.496 (+/-0.001) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.496 (+/-0.001) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.496 (+/-0.001) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.496 (+/-0.001) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.647 (+/-0.460) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.664 (+/-0.388) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.639 (+/-0.326) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.627 (+/-0.185) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.625 (+/-0.179) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.652 (+/-0.123) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.496 (+/-0.001) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.496 (+/-0.001) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.496 (+/-0.001) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.496 (+/-0.001) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.496 (+/-0.001) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.652 (+/-0.313) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.643 (+/-0.306) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.660 (+/-0.290) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.633 (+/-0.120) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.655 (+/-0.126) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.660 (+/-0.136) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.496 (+/-0.001) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.496 (+/-0.001) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.496 (+/-0.001) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.496 (+/-0.001) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.764 (+/-0.421) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.600 (+/-0.167) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.627 (+/-0.291) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.620 (+/-0.172) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.706 (+/-0.214) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.715 (+/-0.210) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.630 (+/-0.189) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.496 (+/-0.001) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.496 (+/-0.001) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.496 (+/-0.001) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.496 (+/-0.001) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.631 (+/-0.196) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.565 (+/-0.085) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.603 (+/-0.155) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.699 (+/-0.211) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.689 (+/-0.248) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.715 (+/-0.210) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.621 (+/-0.177) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.747 (+/-0.502) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.548 (+/-0.097) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.600 (+/-0.112) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.604 (+/-0.143) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.708 (+/-0.211) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.626 (+/-0.188) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.723 (+/-0.208) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.616 (+/-0.170) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.747 (+/-0.502) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.722 (+/-0.473) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.527 (+/-0.071) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.613 (+/-0.206) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.581 (+/-0.166) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.704 (+/-0.224) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.635 (+/-0.190) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.656 (+/-0.138) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.602 (+/-0.159) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.496 (+/-0.001) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.747 (+/-0.502) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.697 (+/-0.437) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.689 (+/-0.436) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.520 (+/-0.060) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.598 (+/-0.184) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.567 (+/-0.126) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.680 (+/-0.235) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.635 (+/-0.190) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.656 (+/-0.138) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.595 (+/-0.159) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.747 (+/-0.502) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.697 (+/-0.437) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.714 (+/-0.352) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.667 (+/-0.311) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.560 (+/-0.294) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.558 (+/-0.153) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.575 (+/-0.159) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.639 (+/-0.136) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.635 (+/-0.190) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.649 (+/-0.142) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.586 (+/-0.154) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.697 (+/-0.437) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.689 (+/-0.299) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.681 (+/-0.297) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.725 (+/-0.321) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.502 (+/-0.445) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.534 (+/-0.146) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.573 (+/-0.170) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.632 (+/-0.144) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.626 (+/-0.176) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.667 (+/-0.253) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.605 (+/-0.279) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.689 (+/-0.299) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.673 (+/-0.293) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.673 (+/-0.293) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.720 (+/-0.326) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.499 (+/-0.446) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.529 (+/-0.145) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.571 (+/-0.164) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.628 (+/-0.140) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.626 (+/-0.176) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.660 (+/-0.258) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.602 (+/-0.281) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.727 (+/-0.397) for {'C': 0.1, 'kernel': 'linear'}
0.625 (+/-0.179) for {'C': 1.0, 'kernel': 'linear'}
0.633 (+/-0.278) for {'C': 10.0, 'kernel': 'linear'}
0.598 (+/-0.284) for {'C': 100.0, 'kernel': 'linear'}
0.578 (+/-0.145) for {'C': 1000.0, 'kernel': 'linear'}
0.608 (+/-0.180) for {'C': 10000.0, 'kernel': 'linear'}
0.634 (+/-0.287) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.500 (+/-0.000) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.500 (+/-0.000) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.500 (+/-0.000) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.500 (+/-0.000) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.500 (+/-0.000) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.500 (+/-0.000) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.550 (+/-0.200) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.616 (+/-0.238) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.615 (+/-0.237) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.656 (+/-0.215) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.697 (+/-0.307) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.500 (+/-0.000) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.500 (+/-0.000) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.500 (+/-0.000) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.500 (+/-0.000) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.500 (+/-0.000) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.575 (+/-0.230) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.616 (+/-0.238) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.615 (+/-0.237) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.656 (+/-0.215) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.697 (+/-0.307) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.746 (+/-0.233) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.500 (+/-0.000) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.500 (+/-0.000) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.500 (+/-0.000) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.500 (+/-0.000) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.500 (+/-0.000) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.640 (+/-0.235) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.640 (+/-0.235) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.656 (+/-0.215) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.705 (+/-0.151) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.747 (+/-0.233) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.747 (+/-0.233) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.500 (+/-0.000) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.500 (+/-0.000) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.500 (+/-0.000) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.500 (+/-0.002) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.657 (+/-0.217) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.678 (+/-0.190) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.679 (+/-0.291) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.697 (+/-0.306) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.772 (+/-0.166) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.772 (+/-0.167) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.697 (+/-0.308) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.500 (+/-0.000) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.500 (+/-0.000) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.500 (+/-0.000) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.500 (+/-0.002) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.697 (+/-0.305) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.676 (+/-0.188) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.701 (+/-0.255) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.771 (+/-0.164) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.747 (+/-0.234) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.772 (+/-0.167) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.696 (+/-0.308) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.617 (+/-0.238) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.679 (+/-0.275) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.760 (+/-0.153) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.737 (+/-0.205) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.771 (+/-0.164) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.697 (+/-0.307) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.772 (+/-0.167) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.696 (+/-0.306) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.617 (+/-0.238) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.617 (+/-0.238) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.612 (+/-0.271) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.725 (+/-0.244) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.677 (+/-0.253) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.771 (+/-0.164) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.697 (+/-0.308) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.747 (+/-0.234) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.696 (+/-0.304) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.500 (+/-0.000) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.617 (+/-0.238) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.616 (+/-0.238) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.616 (+/-0.238) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.581 (+/-0.184) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.682 (+/-0.303) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.659 (+/-0.233) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.770 (+/-0.161) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.697 (+/-0.308) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.747 (+/-0.234) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.695 (+/-0.301) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.617 (+/-0.238) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.616 (+/-0.238) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.657 (+/-0.217) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.670 (+/-0.183) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.572 (+/-0.193) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.670 (+/-0.306) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.649 (+/-0.243) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.768 (+/-0.160) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.697 (+/-0.308) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.746 (+/-0.234) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.694 (+/-0.299) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.616 (+/-0.238) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.657 (+/-0.216) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.657 (+/-0.216) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.726 (+/-0.073) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.545 (+/-0.216) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.611 (+/-0.222) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.645 (+/-0.248) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.767 (+/-0.161) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.697 (+/-0.307) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.746 (+/-0.233) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.693 (+/-0.296) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.657 (+/-0.216) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.656 (+/-0.216) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.656 (+/-0.216) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.697 (+/-0.143) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.514 (+/-0.275) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.576 (+/-0.168) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.644 (+/-0.248) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.767 (+/-0.160) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.697 (+/-0.307) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.746 (+/-0.233) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.692 (+/-0.293) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.657 (+/-0.216) for {'C': 0.1, 'kernel': 'linear'}
0.697 (+/-0.308) for {'C': 1.0, 'kernel': 'linear'}
0.696 (+/-0.306) for {'C': 10.0, 'kernel': 'linear'}
0.691 (+/-0.298) for {'C': 100.0, 'kernel': 'linear'}
0.693 (+/-0.302) for {'C': 1000.0, 'kernel': 'linear'}
0.695 (+/-0.304) for {'C': 10000.0, 'kernel': 'linear'}
0.695 (+/-0.305) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.7721138387336136
循环迭代之前,delta is: [1.16415322e-10 5.30957344e-07]
执行tunemodel()函数前,使用的grid search parameter是:
[{'C': array([ 630957.34448019, 691830.97091894, 758577.57502918,
831763.77110267, 912010.83935591, 1000000. ,
1096478.19614319, 1202264.43461741, 1318256.73855641,
1445439.77074593, 1584893.19246111]), 'kernel': ['rbf'], 'gamma': array([3.98107171e-07, 4.36515832e-07, 4.78630092e-07, 5.24807460e-07,
5.75439937e-07, 6.30957344e-07, 6.91830971e-07, 7.58577575e-07,
8.31763771e-07, 9.12010839e-07, 1.00000000e-06])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.656 (+/-0.138) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.655 (+/-0.126) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.625 (+/-0.179) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.667 (+/-0.136) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.652 (+/-0.123) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.660 (+/-0.136) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.642 (+/-0.158) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.655 (+/-0.126) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.630 (+/-0.189) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.636 (+/-0.169) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.630 (+/-0.189) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.647 (+/-0.124) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.655 (+/-0.126) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.627 (+/-0.185) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.667 (+/-0.136) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.652 (+/-0.123) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.665 (+/-0.145) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.655 (+/-0.126) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.638 (+/-0.157) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.630 (+/-0.189) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.636 (+/-0.169) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.630 (+/-0.189) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.647 (+/-0.124) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.655 (+/-0.126) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.627 (+/-0.185) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.663 (+/-0.178) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.652 (+/-0.123) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.669 (+/-0.142) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.625 (+/-0.179) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.638 (+/-0.157) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.630 (+/-0.189) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.636 (+/-0.169) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.630 (+/-0.189) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.644 (+/-0.120) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.651 (+/-0.127) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.627 (+/-0.185) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.668 (+/-0.185) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.652 (+/-0.123) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.669 (+/-0.142) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.630 (+/-0.189) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.638 (+/-0.157) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.630 (+/-0.189) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.636 (+/-0.169) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.630 (+/-0.189) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.640 (+/-0.164) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.651 (+/-0.127) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.657 (+/-0.134) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.663 (+/-0.178) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.649 (+/-0.126) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.669 (+/-0.142) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.627 (+/-0.185) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.655 (+/-0.126) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.630 (+/-0.189) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.636 (+/-0.169) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.630 (+/-0.189) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.644 (+/-0.120) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.647 (+/-0.124) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.640 (+/-0.164) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.663 (+/-0.178) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.652 (+/-0.123) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.669 (+/-0.142) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.627 (+/-0.185) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.625 (+/-0.179) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.630 (+/-0.189) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.636 (+/-0.169) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.630 (+/-0.189) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.619 (+/-0.169) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.647 (+/-0.124) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.627 (+/-0.185) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.673 (+/-0.190) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.647 (+/-0.124) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.669 (+/-0.142) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.635 (+/-0.198) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.636 (+/-0.159) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.630 (+/-0.189) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.630 (+/-0.189) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.630 (+/-0.189) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.627 (+/-0.185) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.651 (+/-0.127) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.648 (+/-0.164) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.673 (+/-0.190) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.698 (+/-0.213) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.669 (+/-0.142) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.635 (+/-0.198) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.630 (+/-0.189) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.630 (+/-0.189) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.630 (+/-0.189) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.630 (+/-0.189) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.627 (+/-0.185) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.651 (+/-0.127) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.651 (+/-0.211) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.656 (+/-0.218) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.698 (+/-0.213) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.669 (+/-0.142) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.635 (+/-0.198) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.641 (+/-0.169) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.630 (+/-0.189) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.639 (+/-0.202) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.625 (+/-0.179) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.623 (+/-0.184) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.697 (+/-0.216) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.660 (+/-0.177) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.668 (+/-0.185) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.698 (+/-0.213) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.677 (+/-0.150) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.644 (+/-0.209) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.641 (+/-0.169) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.630 (+/-0.189) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.649 (+/-0.181) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.625 (+/-0.179) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.618 (+/-0.172) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.702 (+/-0.219) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.651 (+/-0.167) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.660 (+/-0.219) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.698 (+/-0.213) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.677 (+/-0.150) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.644 (+/-0.209) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.630 (+/-0.189) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.630 (+/-0.189) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.657 (+/-0.191) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.625 (+/-0.179) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.630 (+/-0.189) for {'C': 1.0, 'kernel': 'linear'}
0.655 (+/-0.280) for {'C': 10.0, 'kernel': 'linear'}
0.645 (+/-0.280) for {'C': 100.0, 'kernel': 'linear'}
0.582 (+/-0.153) for {'C': 1000.0, 'kernel': 'linear'}
0.619 (+/-0.185) for {'C': 10000.0, 'kernel': 'linear'}
0.641 (+/-0.286) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.730 (+/-0.231) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.747 (+/-0.233) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.697 (+/-0.307) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.747 (+/-0.233) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.746 (+/-0.233) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.747 (+/-0.233) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.722 (+/-0.278) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.747 (+/-0.233) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.697 (+/-0.308) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.721 (+/-0.277) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.697 (+/-0.308) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.730 (+/-0.231) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.747 (+/-0.233) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.697 (+/-0.307) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.747 (+/-0.233) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.746 (+/-0.233) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.747 (+/-0.234) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.747 (+/-0.233) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.722 (+/-0.277) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.697 (+/-0.308) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.721 (+/-0.277) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.697 (+/-0.308) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.730 (+/-0.231) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.747 (+/-0.233) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.697 (+/-0.307) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.722 (+/-0.278) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.746 (+/-0.233) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.747 (+/-0.234) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.697 (+/-0.308) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.722 (+/-0.277) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.697 (+/-0.308) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.721 (+/-0.277) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.697 (+/-0.308) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.730 (+/-0.230) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.746 (+/-0.233) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.697 (+/-0.307) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.722 (+/-0.279) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.746 (+/-0.233) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.747 (+/-0.234) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.697 (+/-0.308) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.722 (+/-0.277) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.697 (+/-0.308) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.721 (+/-0.277) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.697 (+/-0.308) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.721 (+/-0.277) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.746 (+/-0.233) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.747 (+/-0.233) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.722 (+/-0.278) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.746 (+/-0.233) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.747 (+/-0.234) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.697 (+/-0.307) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.747 (+/-0.233) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.697 (+/-0.308) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.721 (+/-0.277) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.697 (+/-0.308) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.730 (+/-0.230) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.746 (+/-0.233) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.722 (+/-0.277) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.722 (+/-0.278) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.746 (+/-0.233) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.747 (+/-0.234) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.697 (+/-0.307) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.697 (+/-0.308) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.697 (+/-0.308) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.721 (+/-0.277) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.697 (+/-0.308) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.680 (+/-0.294) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.746 (+/-0.233) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.697 (+/-0.307) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.722 (+/-0.279) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.746 (+/-0.233) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.747 (+/-0.234) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.697 (+/-0.307) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.721 (+/-0.277) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.697 (+/-0.308) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.697 (+/-0.308) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.697 (+/-0.308) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.697 (+/-0.306) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.746 (+/-0.233) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.722 (+/-0.277) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.722 (+/-0.279) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.771 (+/-0.165) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.747 (+/-0.234) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.697 (+/-0.307) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.697 (+/-0.308) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.697 (+/-0.308) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.697 (+/-0.308) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.697 (+/-0.308) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.697 (+/-0.306) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.746 (+/-0.233) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.697 (+/-0.308) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.697 (+/-0.309) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.771 (+/-0.165) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.747 (+/-0.234) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.697 (+/-0.307) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.722 (+/-0.277) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.697 (+/-0.308) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.697 (+/-0.308) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.697 (+/-0.308) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.697 (+/-0.306) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.771 (+/-0.166) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.722 (+/-0.278) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.722 (+/-0.278) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.771 (+/-0.165) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.747 (+/-0.234) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.697 (+/-0.307) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.722 (+/-0.277) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.697 (+/-0.308) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.722 (+/-0.277) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.697 (+/-0.308) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.680 (+/-0.294) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.771 (+/-0.166) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.705 (+/-0.270) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.697 (+/-0.308) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.771 (+/-0.165) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.747 (+/-0.234) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.697 (+/-0.307) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.697 (+/-0.308) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.697 (+/-0.308) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.722 (+/-0.278) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.697 (+/-0.308) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.616 (+/-0.239) for {'C': 0.1, 'kernel': 'linear'}
0.697 (+/-0.308) for {'C': 1.0, 'kernel': 'linear'}
0.697 (+/-0.308) for {'C': 10.0, 'kernel': 'linear'}
0.697 (+/-0.307) for {'C': 100.0, 'kernel': 'linear'}
0.693 (+/-0.302) for {'C': 1000.0, 'kernel': 'linear'}
0.696 (+/-0.307) for {'C': 10000.0, 'kernel': 'linear'}
0.696 (+/-0.307) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.7714728130925881
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.689 (+/-0.248) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.697 (+/-0.216) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.661 (+/-0.132) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.651 (+/-0.211) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.707 (+/-0.211) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.715 (+/-0.210) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.639 (+/-0.202) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.631 (+/-0.155) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.627 (+/-0.185) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.630 (+/-0.189) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.621 (+/-0.177) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.623 (+/-0.184) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.697 (+/-0.216) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.656 (+/-0.178) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.651 (+/-0.211) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.703 (+/-0.215) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.723 (+/-0.208) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.639 (+/-0.202) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.631 (+/-0.156) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.625 (+/-0.184) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.636 (+/-0.203) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.621 (+/-0.177) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.619 (+/-0.181) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.702 (+/-0.219) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.664 (+/-0.187) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.660 (+/-0.219) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.698 (+/-0.213) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.718 (+/-0.208) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.647 (+/-0.212) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.631 (+/-0.156) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.625 (+/-0.184) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.631 (+/-0.199) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.615 (+/-0.167) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.623 (+/-0.184) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.706 (+/-0.214) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.660 (+/-0.177) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.660 (+/-0.219) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.698 (+/-0.213) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.718 (+/-0.208) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.634 (+/-0.193) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.609 (+/-0.170) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.625 (+/-0.184) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.626 (+/-0.197) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.616 (+/-0.170) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.623 (+/-0.184) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.706 (+/-0.214) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.668 (+/-0.185) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.660 (+/-0.219) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.698 (+/-0.213) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.718 (+/-0.208) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.634 (+/-0.193) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.609 (+/-0.170) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.622 (+/-0.184) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.631 (+/-0.199) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.616 (+/-0.170) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.626 (+/-0.188) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.703 (+/-0.218) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.668 (+/-0.185) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.660 (+/-0.219) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.693 (+/-0.217) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.723 (+/-0.208) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.630 (+/-0.189) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.608 (+/-0.171) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.622 (+/-0.184) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.631 (+/-0.199) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.616 (+/-0.170) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.626 (+/-0.188) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.706 (+/-0.214) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.668 (+/-0.185) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.660 (+/-0.219) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.693 (+/-0.217) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.668 (+/-0.146) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.630 (+/-0.189) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.608 (+/-0.171) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.630 (+/-0.198) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.627 (+/-0.199) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.612 (+/-0.169) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.630 (+/-0.189) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.714 (+/-0.213) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.668 (+/-0.185) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.660 (+/-0.219) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.693 (+/-0.217) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.660 (+/-0.136) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.626 (+/-0.188) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.607 (+/-0.172) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.624 (+/-0.197) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.625 (+/-0.200) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.617 (+/-0.181) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.635 (+/-0.190) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.711 (+/-0.215) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.668 (+/-0.185) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.660 (+/-0.219) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.697 (+/-0.216) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.660 (+/-0.136) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.626 (+/-0.188) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.604 (+/-0.166) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.623 (+/-0.198) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.624 (+/-0.201) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.617 (+/-0.181) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.635 (+/-0.190) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.711 (+/-0.215) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.668 (+/-0.185) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.660 (+/-0.219) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.698 (+/-0.213) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.656 (+/-0.138) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.626 (+/-0.188) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.601 (+/-0.166) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.617 (+/-0.188) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.615 (+/-0.186) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.607 (+/-0.163) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.635 (+/-0.190) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.714 (+/-0.213) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.651 (+/-0.211) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.660 (+/-0.219) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.693 (+/-0.217) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.656 (+/-0.138) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.626 (+/-0.188) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.601 (+/-0.166) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.617 (+/-0.188) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.615 (+/-0.186) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.602 (+/-0.159) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.727 (+/-0.397) for {'C': 0.1, 'kernel': 'linear'}
0.625 (+/-0.179) for {'C': 1.0, 'kernel': 'linear'}
0.633 (+/-0.278) for {'C': 10.0, 'kernel': 'linear'}
0.598 (+/-0.284) for {'C': 100.0, 'kernel': 'linear'}
0.578 (+/-0.145) for {'C': 1000.0, 'kernel': 'linear'}
0.608 (+/-0.180) for {'C': 10000.0, 'kernel': 'linear'}
0.634 (+/-0.287) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.747 (+/-0.234) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.771 (+/-0.166) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.747 (+/-0.233) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.697 (+/-0.308) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.772 (+/-0.165) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.772 (+/-0.167) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.697 (+/-0.308) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.721 (+/-0.274) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.697 (+/-0.307) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.696 (+/-0.309) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.696 (+/-0.308) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.697 (+/-0.306) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.771 (+/-0.166) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.722 (+/-0.279) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.697 (+/-0.308) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.771 (+/-0.165) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.772 (+/-0.167) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.697 (+/-0.308) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.720 (+/-0.274) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.696 (+/-0.307) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.696 (+/-0.309) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.696 (+/-0.308) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.697 (+/-0.306) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.771 (+/-0.166) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.722 (+/-0.278) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.697 (+/-0.308) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.771 (+/-0.165) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.772 (+/-0.167) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.697 (+/-0.308) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.720 (+/-0.274) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.696 (+/-0.307) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.696 (+/-0.308) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.696 (+/-0.307) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.697 (+/-0.306) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.772 (+/-0.166) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.722 (+/-0.278) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.697 (+/-0.308) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.771 (+/-0.165) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.772 (+/-0.167) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.697 (+/-0.307) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.695 (+/-0.303) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.696 (+/-0.307) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.696 (+/-0.307) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.696 (+/-0.306) for {'C': 831763.77110267, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.697 (+/-0.306) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.772 (+/-0.166) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.722 (+/-0.278) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.698 (+/-0.308) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.771 (+/-0.165) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.772 (+/-0.167) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.697 (+/-0.307) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.695 (+/-0.303) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.696 (+/-0.305) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.696 (+/-0.308) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.696 (+/-0.306) for {'C': 912010.8393559091, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.697 (+/-0.307) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.771 (+/-0.166) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.722 (+/-0.278) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.698 (+/-0.308) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.771 (+/-0.165) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.772 (+/-0.167) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.697 (+/-0.307) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.695 (+/-0.302) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.696 (+/-0.305) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.696 (+/-0.308) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.696 (+/-0.306) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.697 (+/-0.307) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.772 (+/-0.166) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.722 (+/-0.278) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.698 (+/-0.308) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.771 (+/-0.165) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.747 (+/-0.234) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.697 (+/-0.307) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.695 (+/-0.302) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.696 (+/-0.306) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.696 (+/-0.306) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.696 (+/-0.305) for {'C': 1096478.1961431855, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.697 (+/-0.308) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.772 (+/-0.166) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.722 (+/-0.278) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.698 (+/-0.308) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.771 (+/-0.165) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.747 (+/-0.234) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.697 (+/-0.307) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.695 (+/-0.301) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.696 (+/-0.304) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.696 (+/-0.305) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.696 (+/-0.304) for {'C': 1202264.4346174141, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.697 (+/-0.308) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.772 (+/-0.165) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.722 (+/-0.278) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.698 (+/-0.308) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.771 (+/-0.166) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.747 (+/-0.234) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.697 (+/-0.307) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.695 (+/-0.301) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.696 (+/-0.304) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.695 (+/-0.305) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.696 (+/-0.304) for {'C': 1318256.738556406, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.697 (+/-0.308) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.772 (+/-0.165) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.722 (+/-0.278) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.698 (+/-0.308) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.771 (+/-0.166) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.747 (+/-0.234) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.697 (+/-0.307) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.694 (+/-0.299) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.695 (+/-0.303) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.695 (+/-0.305) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.696 (+/-0.304) for {'C': 1445439.7707459272, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.697 (+/-0.308) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.772 (+/-0.165) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.697 (+/-0.308) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.698 (+/-0.308) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.771 (+/-0.166) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.747 (+/-0.234) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.697 (+/-0.307) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.694 (+/-0.300) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.695 (+/-0.303) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.695 (+/-0.306) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.696 (+/-0.304) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.657 (+/-0.216) for {'C': 0.1, 'kernel': 'linear'}
0.697 (+/-0.308) for {'C': 1.0, 'kernel': 'linear'}
0.696 (+/-0.306) for {'C': 10.0, 'kernel': 'linear'}
0.691 (+/-0.298) for {'C': 100.0, 'kernel': 'linear'}
0.693 (+/-0.302) for {'C': 1000.0, 'kernel': 'linear'}
0.695 (+/-0.304) for {'C': 10000.0, 'kernel': 'linear'}
0.695 (+/-0.305) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.7721138387336136
第0轮loop中,tunemodel函数得到的最优参数是: ([{'C': array([630957.34448019, 642687.71731702, 654636.17406727, 666806.76921362,
679203.63261718, 691830.97091894, 704693.06896715, 717794.29127136,
731139.08348342, 744731.97390599, 758577.57502918]), 'kernel': ['rbf'], 'gamma': array([5.75439937e-07, 5.86138165e-07, 5.97035287e-07, 6.08135001e-07,
6.19441075e-07, 6.30957344e-07, 6.42687717e-07, 6.54636174e-07,
6.66806769e-07, 6.79203633e-07, 6.91830971e-07])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}], {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}, 0.7721138387336136)
这是第0次迭代微调C和gamma。
第0次迭代,得到delta: [308169.02908106 0. ]
执行tunemodel()函数前,使用的grid search parameter是:
[{'C': array([630957.34448019, 642687.71731702, 654636.17406727, 666806.76921362,
679203.63261718, 691830.97091894, 704693.06896715, 717794.29127136,
731139.08348342, 744731.97390599, 758577.57502918]), 'kernel': ['rbf'], 'gamma': array([5.75439937e-07, 5.86138165e-07, 5.97035287e-07, 6.08135001e-07,
6.19441075e-07, 6.30957344e-07, 6.42687717e-07, 6.54636174e-07,
6.66806769e-07, 6.79203633e-07, 6.91830971e-07])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.652 (+/-0.123) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.635 (+/-0.153) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 5.861381645140294e-07}
0.635 (+/-0.153) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 5.970352865838373e-07}
0.638 (+/-0.157) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.081350012787182e-07}
0.655 (+/-0.126) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.194410750767815e-07}
0.660 (+/-0.136) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.657 (+/-0.134) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.426877173170207e-07}
0.660 (+/-0.136) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.546361740672759e-07}
0.635 (+/-0.153) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.668067692136228e-07}
0.635 (+/-0.153) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.79203632617185e-07}
0.642 (+/-0.158) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.635 (+/-0.153) for {'C': 642687.7173170191, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.635 (+/-0.153) for {'C': 642687.7173170191, 'kernel': 'rbf', 'gamma': 5.861381645140294e-07}
0.635 (+/-0.153) for {'C': 642687.7173170191, 'kernel': 'rbf', 'gamma': 5.970352865838373e-07}
0.638 (+/-0.157) for {'C': 642687.7173170191, 'kernel': 'rbf', 'gamma': 6.081350012787182e-07}
0.655 (+/-0.126) for {'C': 642687.7173170191, 'kernel': 'rbf', 'gamma': 6.194410750767815e-07}
0.660 (+/-0.136) for {'C': 642687.7173170191, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.657 (+/-0.134) for {'C': 642687.7173170191, 'kernel': 'rbf', 'gamma': 6.426877173170207e-07}
0.660 (+/-0.136) for {'C': 642687.7173170191, 'kernel': 'rbf', 'gamma': 6.546361740672759e-07}
0.635 (+/-0.153) for {'C': 642687.7173170191, 'kernel': 'rbf', 'gamma': 6.668067692136228e-07}
0.635 (+/-0.153) for {'C': 642687.7173170191, 'kernel': 'rbf', 'gamma': 6.79203632617185e-07}
0.655 (+/-0.126) for {'C': 642687.7173170191, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.652 (+/-0.123) for {'C': 654636.1740672742, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.635 (+/-0.153) for {'C': 654636.1740672742, 'kernel': 'rbf', 'gamma': 5.861381645140294e-07}
0.635 (+/-0.153) for {'C': 654636.1740672742, 'kernel': 'rbf', 'gamma': 5.970352865838373e-07}
0.638 (+/-0.157) for {'C': 654636.1740672742, 'kernel': 'rbf', 'gamma': 6.081350012787182e-07}
0.655 (+/-0.126) for {'C': 654636.1740672742, 'kernel': 'rbf', 'gamma': 6.194410750767815e-07}
0.660 (+/-0.136) for {'C': 654636.1740672742, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.657 (+/-0.134) for {'C': 654636.1740672742, 'kernel': 'rbf', 'gamma': 6.426877173170207e-07}
0.660 (+/-0.136) for {'C': 654636.1740672742, 'kernel': 'rbf', 'gamma': 6.546361740672759e-07}
0.635 (+/-0.153) for {'C': 654636.1740672742, 'kernel': 'rbf', 'gamma': 6.668067692136228e-07}
0.635 (+/-0.153) for {'C': 654636.1740672742, 'kernel': 'rbf', 'gamma': 6.79203632617185e-07}
0.655 (+/-0.126) for {'C': 654636.1740672742, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.652 (+/-0.123) for {'C': 666806.7692136227, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.635 (+/-0.153) for {'C': 666806.7692136227, 'kernel': 'rbf', 'gamma': 5.861381645140294e-07}
0.635 (+/-0.153) for {'C': 666806.7692136227, 'kernel': 'rbf', 'gamma': 5.970352865838373e-07}
0.638 (+/-0.157) for {'C': 666806.7692136227, 'kernel': 'rbf', 'gamma': 6.081350012787182e-07}
0.655 (+/-0.126) for {'C': 666806.7692136227, 'kernel': 'rbf', 'gamma': 6.194410750767815e-07}
0.660 (+/-0.136) for {'C': 666806.7692136227, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.657 (+/-0.134) for {'C': 666806.7692136227, 'kernel': 'rbf', 'gamma': 6.426877173170207e-07}
0.660 (+/-0.136) for {'C': 666806.7692136227, 'kernel': 'rbf', 'gamma': 6.546361740672759e-07}
0.635 (+/-0.153) for {'C': 666806.7692136227, 'kernel': 'rbf', 'gamma': 6.668067692136228e-07}
0.635 (+/-0.153) for {'C': 666806.7692136227, 'kernel': 'rbf', 'gamma': 6.79203632617185e-07}
0.655 (+/-0.126) for {'C': 666806.7692136227, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.635 (+/-0.153) for {'C': 679203.6326171849, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.635 (+/-0.153) for {'C': 679203.6326171849, 'kernel': 'rbf', 'gamma': 5.861381645140294e-07}
0.635 (+/-0.153) for {'C': 679203.6326171849, 'kernel': 'rbf', 'gamma': 5.970352865838373e-07}
0.638 (+/-0.157) for {'C': 679203.6326171849, 'kernel': 'rbf', 'gamma': 6.081350012787182e-07}
0.660 (+/-0.136) for {'C': 679203.6326171849, 'kernel': 'rbf', 'gamma': 6.194410750767815e-07}
0.660 (+/-0.136) for {'C': 679203.6326171849, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.657 (+/-0.134) for {'C': 679203.6326171849, 'kernel': 'rbf', 'gamma': 6.426877173170207e-07}
0.660 (+/-0.136) for {'C': 679203.6326171849, 'kernel': 'rbf', 'gamma': 6.546361740672759e-07}
0.635 (+/-0.153) for {'C': 679203.6326171849, 'kernel': 'rbf', 'gamma': 6.668067692136228e-07}
0.635 (+/-0.153) for {'C': 679203.6326171849, 'kernel': 'rbf', 'gamma': 6.79203632617185e-07}
0.655 (+/-0.126) for {'C': 679203.6326171849, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.652 (+/-0.123) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.635 (+/-0.153) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 5.861381645140294e-07}
0.652 (+/-0.123) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 5.970352865838373e-07}
0.655 (+/-0.126) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 6.081350012787182e-07}
0.660 (+/-0.136) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 6.194410750767815e-07}
0.665 (+/-0.145) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.657 (+/-0.134) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 6.426877173170207e-07}
0.660 (+/-0.136) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 6.546361740672759e-07}
0.635 (+/-0.153) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 6.668067692136228e-07}
0.635 (+/-0.153) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 6.79203632617185e-07}
0.655 (+/-0.126) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.652 (+/-0.123) for {'C': 704693.0689671467, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.635 (+/-0.153) for {'C': 704693.0689671467, 'kernel': 'rbf', 'gamma': 5.861381645140294e-07}
0.652 (+/-0.123) for {'C': 704693.0689671467, 'kernel': 'rbf', 'gamma': 5.970352865838373e-07}
0.655 (+/-0.126) for {'C': 704693.0689671467, 'kernel': 'rbf', 'gamma': 6.081350012787182e-07}
0.660 (+/-0.136) for {'C': 704693.0689671467, 'kernel': 'rbf', 'gamma': 6.194410750767815e-07}
0.665 (+/-0.145) for {'C': 704693.0689671467, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.657 (+/-0.134) for {'C': 704693.0689671467, 'kernel': 'rbf', 'gamma': 6.426877173170207e-07}
0.660 (+/-0.136) for {'C': 704693.0689671467, 'kernel': 'rbf', 'gamma': 6.546361740672759e-07}
0.635 (+/-0.153) for {'C': 704693.0689671467, 'kernel': 'rbf', 'gamma': 6.668067692136228e-07}
0.635 (+/-0.153) for {'C': 704693.0689671467, 'kernel': 'rbf', 'gamma': 6.79203632617185e-07}
0.655 (+/-0.126) for {'C': 704693.0689671467, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.652 (+/-0.123) for {'C': 717794.2912713613, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.652 (+/-0.123) for {'C': 717794.2912713613, 'kernel': 'rbf', 'gamma': 5.861381645140294e-07}
0.652 (+/-0.123) for {'C': 717794.2912713613, 'kernel': 'rbf', 'gamma': 5.970352865838373e-07}
0.655 (+/-0.126) for {'C': 717794.2912713613, 'kernel': 'rbf', 'gamma': 6.081350012787182e-07}
0.660 (+/-0.136) for {'C': 717794.2912713613, 'kernel': 'rbf', 'gamma': 6.194410750767815e-07}
0.665 (+/-0.145) for {'C': 717794.2912713613, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.657 (+/-0.134) for {'C': 717794.2912713613, 'kernel': 'rbf', 'gamma': 6.426877173170207e-07}
0.660 (+/-0.136) for {'C': 717794.2912713613, 'kernel': 'rbf', 'gamma': 6.546361740672759e-07}
0.635 (+/-0.153) for {'C': 717794.2912713613, 'kernel': 'rbf', 'gamma': 6.668067692136228e-07}
0.622 (+/-0.174) for {'C': 717794.2912713613, 'kernel': 'rbf', 'gamma': 6.79203632617185e-07}
0.655 (+/-0.126) for {'C': 717794.2912713613, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.652 (+/-0.123) for {'C': 731139.0834834184, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.652 (+/-0.123) for {'C': 731139.0834834184, 'kernel': 'rbf', 'gamma': 5.861381645140294e-07}
0.652 (+/-0.123) for {'C': 731139.0834834184, 'kernel': 'rbf', 'gamma': 5.970352865838373e-07}
0.655 (+/-0.126) for {'C': 731139.0834834184, 'kernel': 'rbf', 'gamma': 6.081350012787182e-07}
0.660 (+/-0.136) for {'C': 731139.0834834184, 'kernel': 'rbf', 'gamma': 6.194410750767815e-07}
0.665 (+/-0.145) for {'C': 731139.0834834184, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.657 (+/-0.134) for {'C': 731139.0834834184, 'kernel': 'rbf', 'gamma': 6.426877173170207e-07}
0.660 (+/-0.136) for {'C': 731139.0834834184, 'kernel': 'rbf', 'gamma': 6.546361740672759e-07}
0.622 (+/-0.174) for {'C': 731139.0834834184, 'kernel': 'rbf', 'gamma': 6.668067692136228e-07}
0.622 (+/-0.174) for {'C': 731139.0834834184, 'kernel': 'rbf', 'gamma': 6.79203632617185e-07}
0.655 (+/-0.126) for {'C': 731139.0834834184, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.652 (+/-0.123) for {'C': 744731.9739059898, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.652 (+/-0.123) for {'C': 744731.9739059898, 'kernel': 'rbf', 'gamma': 5.861381645140294e-07}
0.652 (+/-0.123) for {'C': 744731.9739059898, 'kernel': 'rbf', 'gamma': 5.970352865838373e-07}
0.660 (+/-0.136) for {'C': 744731.9739059898, 'kernel': 'rbf', 'gamma': 6.081350012787182e-07}
0.660 (+/-0.136) for {'C': 744731.9739059898, 'kernel': 'rbf', 'gamma': 6.194410750767815e-07}
0.665 (+/-0.145) for {'C': 744731.9739059898, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.657 (+/-0.134) for {'C': 744731.9739059898, 'kernel': 'rbf', 'gamma': 6.426877173170207e-07}
0.660 (+/-0.136) for {'C': 744731.9739059898, 'kernel': 'rbf', 'gamma': 6.546361740672759e-07}
0.622 (+/-0.174) for {'C': 744731.9739059898, 'kernel': 'rbf', 'gamma': 6.668067692136228e-07}
0.622 (+/-0.174) for {'C': 744731.9739059898, 'kernel': 'rbf', 'gamma': 6.79203632617185e-07}
0.638 (+/-0.157) for {'C': 744731.9739059898, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.652 (+/-0.123) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.652 (+/-0.123) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 5.861381645140294e-07}
0.652 (+/-0.123) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 5.970352865838373e-07}
0.660 (+/-0.136) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 6.081350012787182e-07}
0.660 (+/-0.136) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 6.194410750767815e-07}
0.669 (+/-0.142) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.657 (+/-0.134) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 6.426877173170207e-07}
0.665 (+/-0.145) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 6.546361740672759e-07}
0.622 (+/-0.174) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 6.668067692136228e-07}
0.622 (+/-0.174) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 6.79203632617185e-07}
0.625 (+/-0.179) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.630 (+/-0.189) for {'C': 1.0, 'kernel': 'linear'}
0.655 (+/-0.280) for {'C': 10.0, 'kernel': 'linear'}
0.645 (+/-0.280) for {'C': 100.0, 'kernel': 'linear'}
0.582 (+/-0.153) for {'C': 1000.0, 'kernel': 'linear'}
0.619 (+/-0.185) for {'C': 10000.0, 'kernel': 'linear'}
0.641 (+/-0.286) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.746 (+/-0.233) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.721 (+/-0.277) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 5.861381645140294e-07}
0.721 (+/-0.277) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 5.970352865838373e-07}
0.722 (+/-0.277) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.081350012787182e-07}
0.747 (+/-0.233) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.194410750767815e-07}
0.747 (+/-0.233) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.747 (+/-0.233) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.426877173170207e-07}
0.747 (+/-0.233) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.546361740672759e-07}
0.721 (+/-0.277) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.668067692136228e-07}
0.721 (+/-0.277) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.79203632617185e-07}
0.722 (+/-0.278) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.721 (+/-0.277) for {'C': 642687.7173170191, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.721 (+/-0.277) for {'C': 642687.7173170191, 'kernel': 'rbf', 'gamma': 5.861381645140294e-07}
0.721 (+/-0.277) for {'C': 642687.7173170191, 'kernel': 'rbf', 'gamma': 5.970352865838373e-07}
0.722 (+/-0.277) for {'C': 642687.7173170191, 'kernel': 'rbf', 'gamma': 6.081350012787182e-07}
0.747 (+/-0.233) for {'C': 642687.7173170191, 'kernel': 'rbf', 'gamma': 6.194410750767815e-07}
0.747 (+/-0.233) for {'C': 642687.7173170191, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.747 (+/-0.233) for {'C': 642687.7173170191, 'kernel': 'rbf', 'gamma': 6.426877173170207e-07}
0.747 (+/-0.233) for {'C': 642687.7173170191, 'kernel': 'rbf', 'gamma': 6.546361740672759e-07}
0.721 (+/-0.277) for {'C': 642687.7173170191, 'kernel': 'rbf', 'gamma': 6.668067692136228e-07}
0.721 (+/-0.277) for {'C': 642687.7173170191, 'kernel': 'rbf', 'gamma': 6.79203632617185e-07}
0.747 (+/-0.233) for {'C': 642687.7173170191, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.746 (+/-0.233) for {'C': 654636.1740672742, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.721 (+/-0.277) for {'C': 654636.1740672742, 'kernel': 'rbf', 'gamma': 5.861381645140294e-07}
0.721 (+/-0.277) for {'C': 654636.1740672742, 'kernel': 'rbf', 'gamma': 5.970352865838373e-07}
0.722 (+/-0.277) for {'C': 654636.1740672742, 'kernel': 'rbf', 'gamma': 6.081350012787182e-07}
0.747 (+/-0.233) for {'C': 654636.1740672742, 'kernel': 'rbf', 'gamma': 6.194410750767815e-07}
0.747 (+/-0.233) for {'C': 654636.1740672742, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.747 (+/-0.233) for {'C': 654636.1740672742, 'kernel': 'rbf', 'gamma': 6.426877173170207e-07}
0.747 (+/-0.233) for {'C': 654636.1740672742, 'kernel': 'rbf', 'gamma': 6.546361740672759e-07}
0.721 (+/-0.277) for {'C': 654636.1740672742, 'kernel': 'rbf', 'gamma': 6.668067692136228e-07}
0.721 (+/-0.277) for {'C': 654636.1740672742, 'kernel': 'rbf', 'gamma': 6.79203632617185e-07}
0.747 (+/-0.233) for {'C': 654636.1740672742, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.746 (+/-0.233) for {'C': 666806.7692136227, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.721 (+/-0.277) for {'C': 666806.7692136227, 'kernel': 'rbf', 'gamma': 5.861381645140294e-07}
0.721 (+/-0.277) for {'C': 666806.7692136227, 'kernel': 'rbf', 'gamma': 5.970352865838373e-07}
0.722 (+/-0.277) for {'C': 666806.7692136227, 'kernel': 'rbf', 'gamma': 6.081350012787182e-07}
0.747 (+/-0.233) for {'C': 666806.7692136227, 'kernel': 'rbf', 'gamma': 6.194410750767815e-07}
0.747 (+/-0.233) for {'C': 666806.7692136227, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.747 (+/-0.233) for {'C': 666806.7692136227, 'kernel': 'rbf', 'gamma': 6.426877173170207e-07}
0.747 (+/-0.233) for {'C': 666806.7692136227, 'kernel': 'rbf', 'gamma': 6.546361740672759e-07}
0.721 (+/-0.277) for {'C': 666806.7692136227, 'kernel': 'rbf', 'gamma': 6.668067692136228e-07}
0.721 (+/-0.277) for {'C': 666806.7692136227, 'kernel': 'rbf', 'gamma': 6.79203632617185e-07}
0.747 (+/-0.233) for {'C': 666806.7692136227, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.721 (+/-0.277) for {'C': 679203.6326171849, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.721 (+/-0.277) for {'C': 679203.6326171849, 'kernel': 'rbf', 'gamma': 5.861381645140294e-07}
0.721 (+/-0.277) for {'C': 679203.6326171849, 'kernel': 'rbf', 'gamma': 5.970352865838373e-07}
0.722 (+/-0.277) for {'C': 679203.6326171849, 'kernel': 'rbf', 'gamma': 6.081350012787182e-07}
0.747 (+/-0.234) for {'C': 679203.6326171849, 'kernel': 'rbf', 'gamma': 6.194410750767815e-07}
0.747 (+/-0.233) for {'C': 679203.6326171849, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.747 (+/-0.233) for {'C': 679203.6326171849, 'kernel': 'rbf', 'gamma': 6.426877173170207e-07}
0.747 (+/-0.233) for {'C': 679203.6326171849, 'kernel': 'rbf', 'gamma': 6.546361740672759e-07}
0.721 (+/-0.277) for {'C': 679203.6326171849, 'kernel': 'rbf', 'gamma': 6.668067692136228e-07}
0.721 (+/-0.277) for {'C': 679203.6326171849, 'kernel': 'rbf', 'gamma': 6.79203632617185e-07}
0.747 (+/-0.233) for {'C': 679203.6326171849, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.746 (+/-0.233) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.721 (+/-0.277) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 5.861381645140294e-07}
0.746 (+/-0.233) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 5.970352865838373e-07}
0.747 (+/-0.233) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 6.081350012787182e-07}
0.747 (+/-0.234) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 6.194410750767815e-07}
0.747 (+/-0.234) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.747 (+/-0.233) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 6.426877173170207e-07}
0.747 (+/-0.233) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 6.546361740672759e-07}
0.721 (+/-0.277) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 6.668067692136228e-07}
0.721 (+/-0.277) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 6.79203632617185e-07}
0.747 (+/-0.233) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.746 (+/-0.233) for {'C': 704693.0689671467, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.721 (+/-0.277) for {'C': 704693.0689671467, 'kernel': 'rbf', 'gamma': 5.861381645140294e-07}
0.746 (+/-0.233) for {'C': 704693.0689671467, 'kernel': 'rbf', 'gamma': 5.970352865838373e-07}
0.747 (+/-0.233) for {'C': 704693.0689671467, 'kernel': 'rbf', 'gamma': 6.081350012787182e-07}
0.747 (+/-0.234) for {'C': 704693.0689671467, 'kernel': 'rbf', 'gamma': 6.194410750767815e-07}
0.747 (+/-0.234) for {'C': 704693.0689671467, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.747 (+/-0.233) for {'C': 704693.0689671467, 'kernel': 'rbf', 'gamma': 6.426877173170207e-07}
0.747 (+/-0.233) for {'C': 704693.0689671467, 'kernel': 'rbf', 'gamma': 6.546361740672759e-07}
0.721 (+/-0.277) for {'C': 704693.0689671467, 'kernel': 'rbf', 'gamma': 6.668067692136228e-07}
0.721 (+/-0.277) for {'C': 704693.0689671467, 'kernel': 'rbf', 'gamma': 6.79203632617185e-07}
0.747 (+/-0.233) for {'C': 704693.0689671467, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.746 (+/-0.233) for {'C': 717794.2912713613, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.746 (+/-0.233) for {'C': 717794.2912713613, 'kernel': 'rbf', 'gamma': 5.861381645140294e-07}
0.746 (+/-0.233) for {'C': 717794.2912713613, 'kernel': 'rbf', 'gamma': 5.970352865838373e-07}
0.747 (+/-0.233) for {'C': 717794.2912713613, 'kernel': 'rbf', 'gamma': 6.081350012787182e-07}
0.747 (+/-0.234) for {'C': 717794.2912713613, 'kernel': 'rbf', 'gamma': 6.194410750767815e-07}
0.747 (+/-0.234) for {'C': 717794.2912713613, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.747 (+/-0.233) for {'C': 717794.2912713613, 'kernel': 'rbf', 'gamma': 6.426877173170207e-07}
0.747 (+/-0.233) for {'C': 717794.2912713613, 'kernel': 'rbf', 'gamma': 6.546361740672759e-07}
0.721 (+/-0.277) for {'C': 717794.2912713613, 'kernel': 'rbf', 'gamma': 6.668067692136228e-07}
0.696 (+/-0.307) for {'C': 717794.2912713613, 'kernel': 'rbf', 'gamma': 6.79203632617185e-07}
0.747 (+/-0.233) for {'C': 717794.2912713613, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.746 (+/-0.233) for {'C': 731139.0834834184, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.746 (+/-0.233) for {'C': 731139.0834834184, 'kernel': 'rbf', 'gamma': 5.861381645140294e-07}
0.746 (+/-0.233) for {'C': 731139.0834834184, 'kernel': 'rbf', 'gamma': 5.970352865838373e-07}
0.747 (+/-0.233) for {'C': 731139.0834834184, 'kernel': 'rbf', 'gamma': 6.081350012787182e-07}
0.747 (+/-0.234) for {'C': 731139.0834834184, 'kernel': 'rbf', 'gamma': 6.194410750767815e-07}
0.747 (+/-0.234) for {'C': 731139.0834834184, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.747 (+/-0.233) for {'C': 731139.0834834184, 'kernel': 'rbf', 'gamma': 6.426877173170207e-07}
0.747 (+/-0.233) for {'C': 731139.0834834184, 'kernel': 'rbf', 'gamma': 6.546361740672759e-07}
0.696 (+/-0.307) for {'C': 731139.0834834184, 'kernel': 'rbf', 'gamma': 6.668067692136228e-07}
0.696 (+/-0.307) for {'C': 731139.0834834184, 'kernel': 'rbf', 'gamma': 6.79203632617185e-07}
0.747 (+/-0.233) for {'C': 731139.0834834184, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.746 (+/-0.233) for {'C': 744731.9739059898, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.746 (+/-0.233) for {'C': 744731.9739059898, 'kernel': 'rbf', 'gamma': 5.861381645140294e-07}
0.746 (+/-0.233) for {'C': 744731.9739059898, 'kernel': 'rbf', 'gamma': 5.970352865838373e-07}
0.747 (+/-0.234) for {'C': 744731.9739059898, 'kernel': 'rbf', 'gamma': 6.081350012787182e-07}
0.747 (+/-0.234) for {'C': 744731.9739059898, 'kernel': 'rbf', 'gamma': 6.194410750767815e-07}
0.747 (+/-0.234) for {'C': 744731.9739059898, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.747 (+/-0.233) for {'C': 744731.9739059898, 'kernel': 'rbf', 'gamma': 6.426877173170207e-07}
0.747 (+/-0.233) for {'C': 744731.9739059898, 'kernel': 'rbf', 'gamma': 6.546361740672759e-07}
0.696 (+/-0.307) for {'C': 744731.9739059898, 'kernel': 'rbf', 'gamma': 6.668067692136228e-07}
0.696 (+/-0.307) for {'C': 744731.9739059898, 'kernel': 'rbf', 'gamma': 6.79203632617185e-07}
0.722 (+/-0.277) for {'C': 744731.9739059898, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.746 (+/-0.233) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.746 (+/-0.233) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 5.861381645140294e-07}
0.746 (+/-0.233) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 5.970352865838373e-07}
0.747 (+/-0.234) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 6.081350012787182e-07}
0.747 (+/-0.234) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 6.194410750767815e-07}
0.747 (+/-0.234) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.747 (+/-0.233) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 6.426877173170207e-07}
0.747 (+/-0.234) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 6.546361740672759e-07}
0.696 (+/-0.307) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 6.668067692136228e-07}
0.696 (+/-0.307) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 6.79203632617185e-07}
0.697 (+/-0.308) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.616 (+/-0.239) for {'C': 0.1, 'kernel': 'linear'}
0.697 (+/-0.308) for {'C': 1.0, 'kernel': 'linear'}
0.697 (+/-0.308) for {'C': 10.0, 'kernel': 'linear'}
0.697 (+/-0.307) for {'C': 100.0, 'kernel': 'linear'}
0.693 (+/-0.302) for {'C': 1000.0, 'kernel': 'linear'}
0.696 (+/-0.307) for {'C': 10000.0, 'kernel': 'linear'}
0.696 (+/-0.307) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.7471138387336137
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.707 (+/-0.211) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.702 (+/-0.209) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 5.861381645140294e-07}
0.707 (+/-0.211) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 5.970352865838373e-07}
0.710 (+/-0.209) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.081350012787182e-07}
0.715 (+/-0.210) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.194410750767815e-07}
0.715 (+/-0.210) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.657 (+/-0.134) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.426877173170207e-07}
0.643 (+/-0.167) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.546361740672759e-07}
0.643 (+/-0.167) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.668067692136228e-07}
0.635 (+/-0.198) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.79203632617185e-07}
0.639 (+/-0.202) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.707 (+/-0.211) for {'C': 642687.7173170191, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.707 (+/-0.211) for {'C': 642687.7173170191, 'kernel': 'rbf', 'gamma': 5.861381645140294e-07}
0.707 (+/-0.211) for {'C': 642687.7173170191, 'kernel': 'rbf', 'gamma': 5.970352865838373e-07}
0.710 (+/-0.209) for {'C': 642687.7173170191, 'kernel': 'rbf', 'gamma': 6.081350012787182e-07}
0.715 (+/-0.210) for {'C': 642687.7173170191, 'kernel': 'rbf', 'gamma': 6.194410750767815e-07}
0.715 (+/-0.210) for {'C': 642687.7173170191, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.657 (+/-0.134) for {'C': 642687.7173170191, 'kernel': 'rbf', 'gamma': 6.426877173170207e-07}
0.643 (+/-0.167) for {'C': 642687.7173170191, 'kernel': 'rbf', 'gamma': 6.546361740672759e-07}
0.643 (+/-0.167) for {'C': 642687.7173170191, 'kernel': 'rbf', 'gamma': 6.668067692136228e-07}
0.635 (+/-0.198) for {'C': 642687.7173170191, 'kernel': 'rbf', 'gamma': 6.79203632617185e-07}
0.639 (+/-0.202) for {'C': 642687.7173170191, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.707 (+/-0.211) for {'C': 654636.1740672742, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.707 (+/-0.211) for {'C': 654636.1740672742, 'kernel': 'rbf', 'gamma': 5.861381645140294e-07}
0.707 (+/-0.211) for {'C': 654636.1740672742, 'kernel': 'rbf', 'gamma': 5.970352865838373e-07}
0.710 (+/-0.209) for {'C': 654636.1740672742, 'kernel': 'rbf', 'gamma': 6.081350012787182e-07}
0.715 (+/-0.210) for {'C': 654636.1740672742, 'kernel': 'rbf', 'gamma': 6.194410750767815e-07}
0.715 (+/-0.210) for {'C': 654636.1740672742, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.660 (+/-0.136) for {'C': 654636.1740672742, 'kernel': 'rbf', 'gamma': 6.426877173170207e-07}
0.643 (+/-0.167) for {'C': 654636.1740672742, 'kernel': 'rbf', 'gamma': 6.546361740672759e-07}
0.643 (+/-0.167) for {'C': 654636.1740672742, 'kernel': 'rbf', 'gamma': 6.668067692136228e-07}
0.635 (+/-0.198) for {'C': 654636.1740672742, 'kernel': 'rbf', 'gamma': 6.79203632617185e-07}
0.639 (+/-0.202) for {'C': 654636.1740672742, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.703 (+/-0.215) for {'C': 666806.7692136227, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.707 (+/-0.211) for {'C': 666806.7692136227, 'kernel': 'rbf', 'gamma': 5.861381645140294e-07}
0.710 (+/-0.209) for {'C': 666806.7692136227, 'kernel': 'rbf', 'gamma': 5.970352865838373e-07}
0.710 (+/-0.209) for {'C': 666806.7692136227, 'kernel': 'rbf', 'gamma': 6.081350012787182e-07}
0.715 (+/-0.210) for {'C': 666806.7692136227, 'kernel': 'rbf', 'gamma': 6.194410750767815e-07}
0.715 (+/-0.210) for {'C': 666806.7692136227, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.657 (+/-0.134) for {'C': 666806.7692136227, 'kernel': 'rbf', 'gamma': 6.426877173170207e-07}
0.643 (+/-0.167) for {'C': 666806.7692136227, 'kernel': 'rbf', 'gamma': 6.546361740672759e-07}
0.643 (+/-0.167) for {'C': 666806.7692136227, 'kernel': 'rbf', 'gamma': 6.668067692136228e-07}
0.635 (+/-0.198) for {'C': 666806.7692136227, 'kernel': 'rbf', 'gamma': 6.79203632617185e-07}
0.639 (+/-0.202) for {'C': 666806.7692136227, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.703 (+/-0.215) for {'C': 679203.6326171849, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.707 (+/-0.211) for {'C': 679203.6326171849, 'kernel': 'rbf', 'gamma': 5.861381645140294e-07}
0.710 (+/-0.209) for {'C': 679203.6326171849, 'kernel': 'rbf', 'gamma': 5.970352865838373e-07}
0.710 (+/-0.209) for {'C': 679203.6326171849, 'kernel': 'rbf', 'gamma': 6.081350012787182e-07}
0.715 (+/-0.210) for {'C': 679203.6326171849, 'kernel': 'rbf', 'gamma': 6.194410750767815e-07}
0.715 (+/-0.210) for {'C': 679203.6326171849, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.660 (+/-0.136) for {'C': 679203.6326171849, 'kernel': 'rbf', 'gamma': 6.426877173170207e-07}
0.643 (+/-0.167) for {'C': 679203.6326171849, 'kernel': 'rbf', 'gamma': 6.546361740672759e-07}
0.643 (+/-0.167) for {'C': 679203.6326171849, 'kernel': 'rbf', 'gamma': 6.668067692136228e-07}
0.635 (+/-0.198) for {'C': 679203.6326171849, 'kernel': 'rbf', 'gamma': 6.79203632617185e-07}
0.639 (+/-0.202) for {'C': 679203.6326171849, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.703 (+/-0.215) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.707 (+/-0.211) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 5.861381645140294e-07}
0.710 (+/-0.209) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 5.970352865838373e-07}
0.710 (+/-0.209) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 6.081350012787182e-07}
0.715 (+/-0.210) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 6.194410750767815e-07}
0.723 (+/-0.208) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.660 (+/-0.136) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 6.426877173170207e-07}
0.643 (+/-0.167) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 6.546361740672759e-07}
0.639 (+/-0.167) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 6.668067692136228e-07}
0.635 (+/-0.198) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 6.79203632617185e-07}
0.639 (+/-0.202) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.703 (+/-0.215) for {'C': 704693.0689671467, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.707 (+/-0.211) for {'C': 704693.0689671467, 'kernel': 'rbf', 'gamma': 5.861381645140294e-07}
0.707 (+/-0.211) for {'C': 704693.0689671467, 'kernel': 'rbf', 'gamma': 5.970352865838373e-07}
0.710 (+/-0.209) for {'C': 704693.0689671467, 'kernel': 'rbf', 'gamma': 6.081350012787182e-07}
0.715 (+/-0.210) for {'C': 704693.0689671467, 'kernel': 'rbf', 'gamma': 6.194410750767815e-07}
0.723 (+/-0.208) for {'C': 704693.0689671467, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.660 (+/-0.136) for {'C': 704693.0689671467, 'kernel': 'rbf', 'gamma': 6.426877173170207e-07}
0.643 (+/-0.167) for {'C': 704693.0689671467, 'kernel': 'rbf', 'gamma': 6.546361740672759e-07}
0.643 (+/-0.167) for {'C': 704693.0689671467, 'kernel': 'rbf', 'gamma': 6.668067692136228e-07}
0.635 (+/-0.198) for {'C': 704693.0689671467, 'kernel': 'rbf', 'gamma': 6.79203632617185e-07}
0.647 (+/-0.212) for {'C': 704693.0689671467, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.703 (+/-0.215) for {'C': 717794.2912713613, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.707 (+/-0.211) for {'C': 717794.2912713613, 'kernel': 'rbf', 'gamma': 5.861381645140294e-07}
0.707 (+/-0.211) for {'C': 717794.2912713613, 'kernel': 'rbf', 'gamma': 5.970352865838373e-07}
0.710 (+/-0.209) for {'C': 717794.2912713613, 'kernel': 'rbf', 'gamma': 6.081350012787182e-07}
0.715 (+/-0.210) for {'C': 717794.2912713613, 'kernel': 'rbf', 'gamma': 6.194410750767815e-07}
0.723 (+/-0.208) for {'C': 717794.2912713613, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.660 (+/-0.136) for {'C': 717794.2912713613, 'kernel': 'rbf', 'gamma': 6.426877173170207e-07}
0.643 (+/-0.167) for {'C': 717794.2912713613, 'kernel': 'rbf', 'gamma': 6.546361740672759e-07}
0.643 (+/-0.167) for {'C': 717794.2912713613, 'kernel': 'rbf', 'gamma': 6.668067692136228e-07}
0.635 (+/-0.198) for {'C': 717794.2912713613, 'kernel': 'rbf', 'gamma': 6.79203632617185e-07}
0.647 (+/-0.212) for {'C': 717794.2912713613, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.703 (+/-0.215) for {'C': 731139.0834834184, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.707 (+/-0.211) for {'C': 731139.0834834184, 'kernel': 'rbf', 'gamma': 5.861381645140294e-07}
0.710 (+/-0.209) for {'C': 731139.0834834184, 'kernel': 'rbf', 'gamma': 5.970352865838373e-07}
0.710 (+/-0.209) for {'C': 731139.0834834184, 'kernel': 'rbf', 'gamma': 6.081350012787182e-07}
0.715 (+/-0.210) for {'C': 731139.0834834184, 'kernel': 'rbf', 'gamma': 6.194410750767815e-07}
0.718 (+/-0.208) for {'C': 731139.0834834184, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.660 (+/-0.136) for {'C': 731139.0834834184, 'kernel': 'rbf', 'gamma': 6.426877173170207e-07}
0.643 (+/-0.167) for {'C': 731139.0834834184, 'kernel': 'rbf', 'gamma': 6.546361740672759e-07}
0.647 (+/-0.167) for {'C': 731139.0834834184, 'kernel': 'rbf', 'gamma': 6.668067692136228e-07}
0.635 (+/-0.198) for {'C': 731139.0834834184, 'kernel': 'rbf', 'gamma': 6.79203632617185e-07}
0.647 (+/-0.212) for {'C': 731139.0834834184, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.703 (+/-0.215) for {'C': 744731.9739059898, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.707 (+/-0.211) for {'C': 744731.9739059898, 'kernel': 'rbf', 'gamma': 5.861381645140294e-07}
0.710 (+/-0.209) for {'C': 744731.9739059898, 'kernel': 'rbf', 'gamma': 5.970352865838373e-07}
0.710 (+/-0.209) for {'C': 744731.9739059898, 'kernel': 'rbf', 'gamma': 6.081350012787182e-07}
0.715 (+/-0.210) for {'C': 744731.9739059898, 'kernel': 'rbf', 'gamma': 6.194410750767815e-07}
0.718 (+/-0.208) for {'C': 744731.9739059898, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.660 (+/-0.136) for {'C': 744731.9739059898, 'kernel': 'rbf', 'gamma': 6.426877173170207e-07}
0.643 (+/-0.167) for {'C': 744731.9739059898, 'kernel': 'rbf', 'gamma': 6.546361740672759e-07}
0.647 (+/-0.167) for {'C': 744731.9739059898, 'kernel': 'rbf', 'gamma': 6.668067692136228e-07}
0.630 (+/-0.189) for {'C': 744731.9739059898, 'kernel': 'rbf', 'gamma': 6.79203632617185e-07}
0.647 (+/-0.212) for {'C': 744731.9739059898, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.698 (+/-0.213) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.707 (+/-0.211) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 5.861381645140294e-07}
0.710 (+/-0.209) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 5.970352865838373e-07}
0.710 (+/-0.209) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 6.081350012787182e-07}
0.715 (+/-0.210) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 6.194410750767815e-07}
0.718 (+/-0.208) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.660 (+/-0.136) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 6.426877173170207e-07}
0.643 (+/-0.167) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 6.546361740672759e-07}
0.643 (+/-0.167) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 6.668067692136228e-07}
0.639 (+/-0.201) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 6.79203632617185e-07}
0.647 (+/-0.212) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.727 (+/-0.397) for {'C': 0.1, 'kernel': 'linear'}
0.625 (+/-0.179) for {'C': 1.0, 'kernel': 'linear'}
0.633 (+/-0.278) for {'C': 10.0, 'kernel': 'linear'}
0.598 (+/-0.284) for {'C': 100.0, 'kernel': 'linear'}
0.578 (+/-0.145) for {'C': 1000.0, 'kernel': 'linear'}
0.608 (+/-0.180) for {'C': 10000.0, 'kernel': 'linear'}
0.634 (+/-0.287) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.772 (+/-0.165) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.771 (+/-0.165) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 5.861381645140294e-07}
0.772 (+/-0.165) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 5.970352865838373e-07}
0.772 (+/-0.166) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.081350012787182e-07}
0.772 (+/-0.167) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.194410750767815e-07}
0.772 (+/-0.167) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.747 (+/-0.233) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.426877173170207e-07}
0.722 (+/-0.277) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.546361740672759e-07}
0.722 (+/-0.277) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.668067692136228e-07}
0.697 (+/-0.307) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.79203632617185e-07}
0.697 (+/-0.308) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.772 (+/-0.165) for {'C': 642687.7173170191, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.772 (+/-0.165) for {'C': 642687.7173170191, 'kernel': 'rbf', 'gamma': 5.861381645140294e-07}
0.772 (+/-0.165) for {'C': 642687.7173170191, 'kernel': 'rbf', 'gamma': 5.970352865838373e-07}
0.772 (+/-0.166) for {'C': 642687.7173170191, 'kernel': 'rbf', 'gamma': 6.081350012787182e-07}
0.772 (+/-0.167) for {'C': 642687.7173170191, 'kernel': 'rbf', 'gamma': 6.194410750767815e-07}
0.772 (+/-0.167) for {'C': 642687.7173170191, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.747 (+/-0.233) for {'C': 642687.7173170191, 'kernel': 'rbf', 'gamma': 6.426877173170207e-07}
0.722 (+/-0.277) for {'C': 642687.7173170191, 'kernel': 'rbf', 'gamma': 6.546361740672759e-07}
0.722 (+/-0.277) for {'C': 642687.7173170191, 'kernel': 'rbf', 'gamma': 6.668067692136228e-07}
0.697 (+/-0.307) for {'C': 642687.7173170191, 'kernel': 'rbf', 'gamma': 6.79203632617185e-07}
0.697 (+/-0.308) for {'C': 642687.7173170191, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.772 (+/-0.165) for {'C': 654636.1740672742, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.772 (+/-0.165) for {'C': 654636.1740672742, 'kernel': 'rbf', 'gamma': 5.861381645140294e-07}
0.772 (+/-0.165) for {'C': 654636.1740672742, 'kernel': 'rbf', 'gamma': 5.970352865838373e-07}
0.772 (+/-0.166) for {'C': 654636.1740672742, 'kernel': 'rbf', 'gamma': 6.081350012787182e-07}
0.772 (+/-0.167) for {'C': 654636.1740672742, 'kernel': 'rbf', 'gamma': 6.194410750767815e-07}
0.772 (+/-0.167) for {'C': 654636.1740672742, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.747 (+/-0.233) for {'C': 654636.1740672742, 'kernel': 'rbf', 'gamma': 6.426877173170207e-07}
0.722 (+/-0.277) for {'C': 654636.1740672742, 'kernel': 'rbf', 'gamma': 6.546361740672759e-07}
0.722 (+/-0.277) for {'C': 654636.1740672742, 'kernel': 'rbf', 'gamma': 6.668067692136228e-07}
0.697 (+/-0.307) for {'C': 654636.1740672742, 'kernel': 'rbf', 'gamma': 6.79203632617185e-07}
0.697 (+/-0.308) for {'C': 654636.1740672742, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.771 (+/-0.165) for {'C': 666806.7692136227, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.772 (+/-0.165) for {'C': 666806.7692136227, 'kernel': 'rbf', 'gamma': 5.861381645140294e-07}
0.772 (+/-0.166) for {'C': 666806.7692136227, 'kernel': 'rbf', 'gamma': 5.970352865838373e-07}
0.772 (+/-0.166) for {'C': 666806.7692136227, 'kernel': 'rbf', 'gamma': 6.081350012787182e-07}
0.772 (+/-0.167) for {'C': 666806.7692136227, 'kernel': 'rbf', 'gamma': 6.194410750767815e-07}
0.772 (+/-0.167) for {'C': 666806.7692136227, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.747 (+/-0.233) for {'C': 666806.7692136227, 'kernel': 'rbf', 'gamma': 6.426877173170207e-07}
0.722 (+/-0.277) for {'C': 666806.7692136227, 'kernel': 'rbf', 'gamma': 6.546361740672759e-07}
0.722 (+/-0.277) for {'C': 666806.7692136227, 'kernel': 'rbf', 'gamma': 6.668067692136228e-07}
0.697 (+/-0.307) for {'C': 666806.7692136227, 'kernel': 'rbf', 'gamma': 6.79203632617185e-07}
0.697 (+/-0.308) for {'C': 666806.7692136227, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.771 (+/-0.165) for {'C': 679203.6326171849, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.772 (+/-0.165) for {'C': 679203.6326171849, 'kernel': 'rbf', 'gamma': 5.861381645140294e-07}
0.772 (+/-0.166) for {'C': 679203.6326171849, 'kernel': 'rbf', 'gamma': 5.970352865838373e-07}
0.772 (+/-0.166) for {'C': 679203.6326171849, 'kernel': 'rbf', 'gamma': 6.081350012787182e-07}
0.772 (+/-0.167) for {'C': 679203.6326171849, 'kernel': 'rbf', 'gamma': 6.194410750767815e-07}
0.772 (+/-0.167) for {'C': 679203.6326171849, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.747 (+/-0.233) for {'C': 679203.6326171849, 'kernel': 'rbf', 'gamma': 6.426877173170207e-07}
0.722 (+/-0.277) for {'C': 679203.6326171849, 'kernel': 'rbf', 'gamma': 6.546361740672759e-07}
0.722 (+/-0.277) for {'C': 679203.6326171849, 'kernel': 'rbf', 'gamma': 6.668067692136228e-07}
0.697 (+/-0.307) for {'C': 679203.6326171849, 'kernel': 'rbf', 'gamma': 6.79203632617185e-07}
0.697 (+/-0.308) for {'C': 679203.6326171849, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.771 (+/-0.165) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.772 (+/-0.165) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 5.861381645140294e-07}
0.772 (+/-0.166) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 5.970352865838373e-07}
0.772 (+/-0.166) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 6.081350012787182e-07}
0.772 (+/-0.167) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 6.194410750767815e-07}
0.772 (+/-0.167) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.747 (+/-0.233) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 6.426877173170207e-07}
0.722 (+/-0.277) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 6.546361740672759e-07}
0.721 (+/-0.277) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 6.668067692136228e-07}
0.697 (+/-0.307) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 6.79203632617185e-07}
0.697 (+/-0.308) for {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.771 (+/-0.165) for {'C': 704693.0689671467, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.772 (+/-0.165) for {'C': 704693.0689671467, 'kernel': 'rbf', 'gamma': 5.861381645140294e-07}
0.772 (+/-0.165) for {'C': 704693.0689671467, 'kernel': 'rbf', 'gamma': 5.970352865838373e-07}
0.772 (+/-0.166) for {'C': 704693.0689671467, 'kernel': 'rbf', 'gamma': 6.081350012787182e-07}
0.772 (+/-0.167) for {'C': 704693.0689671467, 'kernel': 'rbf', 'gamma': 6.194410750767815e-07}
0.772 (+/-0.167) for {'C': 704693.0689671467, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.747 (+/-0.233) for {'C': 704693.0689671467, 'kernel': 'rbf', 'gamma': 6.426877173170207e-07}
0.722 (+/-0.277) for {'C': 704693.0689671467, 'kernel': 'rbf', 'gamma': 6.546361740672759e-07}
0.722 (+/-0.277) for {'C': 704693.0689671467, 'kernel': 'rbf', 'gamma': 6.668067692136228e-07}
0.697 (+/-0.307) for {'C': 704693.0689671467, 'kernel': 'rbf', 'gamma': 6.79203632617185e-07}
0.697 (+/-0.308) for {'C': 704693.0689671467, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.771 (+/-0.165) for {'C': 717794.2912713613, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.772 (+/-0.165) for {'C': 717794.2912713613, 'kernel': 'rbf', 'gamma': 5.861381645140294e-07}
0.772 (+/-0.165) for {'C': 717794.2912713613, 'kernel': 'rbf', 'gamma': 5.970352865838373e-07}
0.772 (+/-0.166) for {'C': 717794.2912713613, 'kernel': 'rbf', 'gamma': 6.081350012787182e-07}
0.772 (+/-0.167) for {'C': 717794.2912713613, 'kernel': 'rbf', 'gamma': 6.194410750767815e-07}
0.772 (+/-0.167) for {'C': 717794.2912713613, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.747 (+/-0.233) for {'C': 717794.2912713613, 'kernel': 'rbf', 'gamma': 6.426877173170207e-07}
0.722 (+/-0.277) for {'C': 717794.2912713613, 'kernel': 'rbf', 'gamma': 6.546361740672759e-07}
0.722 (+/-0.277) for {'C': 717794.2912713613, 'kernel': 'rbf', 'gamma': 6.668067692136228e-07}
0.697 (+/-0.307) for {'C': 717794.2912713613, 'kernel': 'rbf', 'gamma': 6.79203632617185e-07}
0.697 (+/-0.308) for {'C': 717794.2912713613, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.771 (+/-0.165) for {'C': 731139.0834834184, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.772 (+/-0.165) for {'C': 731139.0834834184, 'kernel': 'rbf', 'gamma': 5.861381645140294e-07}
0.772 (+/-0.166) for {'C': 731139.0834834184, 'kernel': 'rbf', 'gamma': 5.970352865838373e-07}
0.772 (+/-0.166) for {'C': 731139.0834834184, 'kernel': 'rbf', 'gamma': 6.081350012787182e-07}
0.772 (+/-0.167) for {'C': 731139.0834834184, 'kernel': 'rbf', 'gamma': 6.194410750767815e-07}
0.772 (+/-0.167) for {'C': 731139.0834834184, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.747 (+/-0.233) for {'C': 731139.0834834184, 'kernel': 'rbf', 'gamma': 6.426877173170207e-07}
0.722 (+/-0.277) for {'C': 731139.0834834184, 'kernel': 'rbf', 'gamma': 6.546361740672759e-07}
0.722 (+/-0.277) for {'C': 731139.0834834184, 'kernel': 'rbf', 'gamma': 6.668067692136228e-07}
0.697 (+/-0.307) for {'C': 731139.0834834184, 'kernel': 'rbf', 'gamma': 6.79203632617185e-07}
0.697 (+/-0.308) for {'C': 731139.0834834184, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.771 (+/-0.165) for {'C': 744731.9739059898, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.772 (+/-0.165) for {'C': 744731.9739059898, 'kernel': 'rbf', 'gamma': 5.861381645140294e-07}
0.772 (+/-0.166) for {'C': 744731.9739059898, 'kernel': 'rbf', 'gamma': 5.970352865838373e-07}
0.772 (+/-0.166) for {'C': 744731.9739059898, 'kernel': 'rbf', 'gamma': 6.081350012787182e-07}
0.772 (+/-0.167) for {'C': 744731.9739059898, 'kernel': 'rbf', 'gamma': 6.194410750767815e-07}
0.772 (+/-0.167) for {'C': 744731.9739059898, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.747 (+/-0.233) for {'C': 744731.9739059898, 'kernel': 'rbf', 'gamma': 6.426877173170207e-07}
0.722 (+/-0.277) for {'C': 744731.9739059898, 'kernel': 'rbf', 'gamma': 6.546361740672759e-07}
0.722 (+/-0.277) for {'C': 744731.9739059898, 'kernel': 'rbf', 'gamma': 6.668067692136228e-07}
0.697 (+/-0.307) for {'C': 744731.9739059898, 'kernel': 'rbf', 'gamma': 6.79203632617185e-07}
0.697 (+/-0.308) for {'C': 744731.9739059898, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.771 (+/-0.165) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.772 (+/-0.165) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 5.861381645140294e-07}
0.772 (+/-0.166) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 5.970352865838373e-07}
0.772 (+/-0.166) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 6.081350012787182e-07}
0.772 (+/-0.167) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 6.194410750767815e-07}
0.772 (+/-0.167) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.747 (+/-0.233) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 6.426877173170207e-07}
0.722 (+/-0.277) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 6.546361740672759e-07}
0.722 (+/-0.277) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 6.668067692136228e-07}
0.697 (+/-0.307) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 6.79203632617185e-07}
0.697 (+/-0.308) for {'C': 758577.5750291842, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.657 (+/-0.216) for {'C': 0.1, 'kernel': 'linear'}
0.697 (+/-0.308) for {'C': 1.0, 'kernel': 'linear'}
0.696 (+/-0.306) for {'C': 10.0, 'kernel': 'linear'}
0.691 (+/-0.298) for {'C': 100.0, 'kernel': 'linear'}
0.693 (+/-0.302) for {'C': 1000.0, 'kernel': 'linear'}
0.695 (+/-0.304) for {'C': 10000.0, 'kernel': 'linear'}
0.695 (+/-0.305) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.7721138387336136
第1轮loop中,tunemodel函数得到的最优参数是: ([{'C': array([679203.63261718, 681710.52630585, 684226.67276595, 686752.10614882,
689286.86073184, 691830.97091894, 694384.47124098, 696947.39635632,
699519.78105121, 702101.66024031, 704693.06896715]), 'kernel': ['rbf'], 'gamma': array([6.19441075e-07, 6.21727389e-07, 6.24022142e-07, 6.26325365e-07,
6.28637088e-07, 6.30957344e-07, 6.33286164e-07, 6.35623580e-07,
6.37969623e-07, 6.40324324e-07, 6.42687717e-07])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}], {'C': 691830.9709189365, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}, 0.7721138387336136)
这是第1次迭代微调C和gamma。
第1次迭代,得到delta: [0. 0.]
SVC模型clf_op_iter的参数是: {'kernel': 'rbf', 'decision_function_shape': 'ovr', 'class_weight': {-1: 15}, 'tol': 0.001, 'gamma': 6.309573444801931e-07, 'random_state': 0, 'cache_size': 200, 'verbose': False, 'degree': 3, 'C': 691830.9709189365, 'probability': False, 'shrinking': True, 'coef0': 0.0, 'max_iter': -1}
训练模型SVC对训练样本的预测准确率: 0.9142452161587526
测试集中,预测为舞弊样本的有: (array([ 112, 474, 475, 1230, 1247, 1248, 1252, 1255], dtype=int64),)
测试集中,实际为舞弊样本的有: (array([1246, 1247, 1248, 1249, 1250, 1251, 1252, 1253, 1254, 1255, 1256],
dtype=int64),)
预测的舞弊样本数目: 8
训练模型SVC对测试样本的预测准确率: 0.9227498228206945
以上是第46次特征筛选。
第46次特征筛选,AUC值是: 0.6802130453815847
X_train_iter_svc.shape is: (1257, 6)
X_test_iter_svc.shape is: (1257, 6)
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.581 (+/-0.180) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.537 (+/-0.134) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.630 (+/-0.189) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.614 (+/-0.236) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.498 (+/-0.005) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1000.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'rbf', 'gamma': 1000.0}
0.570 (+/-0.228) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.697 (+/-0.308) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.498 (+/-0.005) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1000.0}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6967933259131008
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.625 (+/-0.179) for {'C': 1.0, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.562 (+/-0.158) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.697 (+/-0.308) for {'C': 1.0, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.685 (+/-0.298) for {'C': 1000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 1.0, 'kernel': 'linear'}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.6966325542089208
RBF的粗grid search最佳超参: {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001} RBF的粗grid search最高分值: 0.6967933259131008
linear的粗grid search最佳超参: {'C': 1.0, 'kernel': 'linear'} linear的粗grid search最高分值: 0.6966325542089208
粗grid search得到的parameter是:
[{'C': array([1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02, 1.e+03, 1.e+04, 1.e+05,
1.e+06, 1.e+07, 1.e+08]), 'kernel': ['rbf'], 'gamma': array([1.e-08, 1.e-07, 1.e-06, 1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01,
1.e+00, 1.e+01, 1.e+02])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.689 (+/-0.436) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.639 (+/-0.326) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.581 (+/-0.180) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.639 (+/-0.326) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.630 (+/-0.189) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.585 (+/-0.186) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.505 (+/-0.051) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.639 (+/-0.326) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.630 (+/-0.189) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.652 (+/-0.284) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.538 (+/-0.171) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.639 (+/-0.326) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.630 (+/-0.189) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.650 (+/-0.281) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.592 (+/-0.299) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.537 (+/-0.134) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.639 (+/-0.326) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.630 (+/-0.189) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.650 (+/-0.281) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.669 (+/-0.295) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.539 (+/-0.135) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.546 (+/-0.171) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.639 (+/-0.326) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.630 (+/-0.189) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.655 (+/-0.280) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.644 (+/-0.282) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.564 (+/-0.177) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.530 (+/-0.117) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.537 (+/-0.135) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.666 (+/-0.275) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.630 (+/-0.189) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.650 (+/-0.281) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.637 (+/-0.288) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.605 (+/-0.196) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.528 (+/-0.108) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.538 (+/-0.113) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.521 (+/-0.106) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.722 (+/-0.473) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.589 (+/-0.194) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.633 (+/-0.281) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.615 (+/-0.293) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.568 (+/-0.155) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.558 (+/-0.124) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.502 (+/-0.035) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.528 (+/-0.108) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.521 (+/-0.106) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.721 (+/-0.328) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.572 (+/-0.190) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.629 (+/-0.279) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.609 (+/-0.296) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.621 (+/-0.380) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.563 (+/-0.159) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.507 (+/-0.061) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.528 (+/-0.108) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.521 (+/-0.106) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.630 (+/-0.189) for {'C': 1.0, 'kernel': 'linear'}
0.650 (+/-0.281) for {'C': 10.0, 'kernel': 'linear'}
0.648 (+/-0.282) for {'C': 100.0, 'kernel': 'linear'}
0.619 (+/-0.177) for {'C': 1000.0, 'kernel': 'linear'}
0.598 (+/-0.159) for {'C': 10000.0, 'kernel': 'linear'}
0.585 (+/-0.147) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.616 (+/-0.238) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.498 (+/-0.003) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.002) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.615 (+/-0.237) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.236) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.499 (+/-0.002) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.499 (+/-0.002) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.615 (+/-0.237) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.308) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.614 (+/-0.236) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.522 (+/-0.147) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.012) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.615 (+/-0.237) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.308) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.656 (+/-0.216) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.536 (+/-0.174) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.008) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.497 (+/-0.006) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.615 (+/-0.237) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.308) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.308) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.613 (+/-0.232) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.570 (+/-0.228) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.493 (+/-0.012) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.497 (+/-0.005) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.615 (+/-0.237) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.308) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.308) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.673 (+/-0.240) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.553 (+/-0.185) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.571 (+/-0.229) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.495 (+/-0.008) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.497 (+/-0.005) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.615 (+/-0.237) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.697 (+/-0.308) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.308) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.696 (+/-0.307) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.611 (+/-0.229) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.562 (+/-0.203) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.569 (+/-0.231) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.495 (+/-0.008) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.497 (+/-0.005) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.680 (+/-0.193) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.697 (+/-0.308) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.697 (+/-0.308) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.696 (+/-0.303) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.693 (+/-0.303) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.551 (+/-0.184) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.587 (+/-0.224) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.543 (+/-0.200) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.495 (+/-0.008) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.497 (+/-0.005) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.616 (+/-0.239) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.662 (+/-0.228) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.696 (+/-0.303) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.694 (+/-0.299) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.689 (+/-0.292) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.687 (+/-0.293) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.519 (+/-0.145) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.558 (+/-0.209) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.543 (+/-0.200) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.495 (+/-0.008) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.497 (+/-0.005) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.706 (+/-0.269) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.633 (+/-0.241) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.696 (+/-0.301) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.692 (+/-0.297) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.687 (+/-0.289) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.684 (+/-0.286) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.519 (+/-0.150) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.556 (+/-0.212) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.543 (+/-0.200) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.495 (+/-0.008) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.497 (+/-0.005) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.616 (+/-0.239) for {'C': 0.1, 'kernel': 'linear'}
0.697 (+/-0.308) for {'C': 1.0, 'kernel': 'linear'}
0.697 (+/-0.308) for {'C': 10.0, 'kernel': 'linear'}
0.697 (+/-0.307) for {'C': 100.0, 'kernel': 'linear'}
0.696 (+/-0.307) for {'C': 1000.0, 'kernel': 'linear'}
0.695 (+/-0.307) for {'C': 10000.0, 'kernel': 'linear'}
0.694 (+/-0.307) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.7059284565916398
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.606 (+/-0.146) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.513 (+/-0.101) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.625 (+/-0.179) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.631 (+/-0.197) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.625 (+/-0.179) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.621 (+/-0.177) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.573 (+/-0.200) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.505 (+/-0.051) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.625 (+/-0.179) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.625 (+/-0.179) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.631 (+/-0.292) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.538 (+/-0.171) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.625 (+/-0.179) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.625 (+/-0.179) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.623 (+/-0.288) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.586 (+/-0.294) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.530 (+/-0.113) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.625 (+/-0.179) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.621 (+/-0.177) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.619 (+/-0.286) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.604 (+/-0.306) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.539 (+/-0.135) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.522 (+/-0.105) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.625 (+/-0.179) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.621 (+/-0.177) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.615 (+/-0.285) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.594 (+/-0.295) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.559 (+/-0.163) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.523 (+/-0.102) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.526 (+/-0.106) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.600 (+/-0.112) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.612 (+/-0.164) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.604 (+/-0.299) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.588 (+/-0.307) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.564 (+/-0.187) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.519 (+/-0.068) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.530 (+/-0.107) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.521 (+/-0.106) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.689 (+/-0.299) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.529 (+/-0.146) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.598 (+/-0.281) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.590 (+/-0.307) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.555 (+/-0.160) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.536 (+/-0.095) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.502 (+/-0.035) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.528 (+/-0.108) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.521 (+/-0.106) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.666 (+/-0.295) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.526 (+/-0.147) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.592 (+/-0.284) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.587 (+/-0.308) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.588 (+/-0.308) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.536 (+/-0.096) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.507 (+/-0.061) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.528 (+/-0.108) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.521 (+/-0.106) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.001) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.727 (+/-0.397) for {'C': 0.1, 'kernel': 'linear'}
0.625 (+/-0.179) for {'C': 1.0, 'kernel': 'linear'}
0.632 (+/-0.282) for {'C': 10.0, 'kernel': 'linear'}
0.597 (+/-0.290) for {'C': 100.0, 'kernel': 'linear'}
0.562 (+/-0.158) for {'C': 1000.0, 'kernel': 'linear'}
0.558 (+/-0.134) for {'C': 10000.0, 'kernel': 'linear'}
0.544 (+/-0.100) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 0.1}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 1.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 10.0}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.01}
0.500 (+/-0.000) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 0.1}
0.655 (+/-0.215) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 1.0}
0.523 (+/-0.149) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 10.0}
0.498 (+/-0.003) for {'C': 0.1, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.001}
0.500 (+/-0.000) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.01}
0.697 (+/-0.307) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 0.1}
0.655 (+/-0.216) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 1.0}
0.495 (+/-0.009) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 10.0}
0.497 (+/-0.010) for {'C': 1.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.500 (+/-0.000) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.307) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.01}
0.696 (+/-0.308) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 0.1}
0.612 (+/-0.231) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 1.0}
0.522 (+/-0.147) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 10.0}
0.496 (+/-0.012) for {'C': 10.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.500 (+/-0.000) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.307) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.001}
0.697 (+/-0.308) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.01}
0.670 (+/-0.238) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 0.1}
0.535 (+/-0.175) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 1.0}
0.488 (+/-0.024) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 10.0}
0.497 (+/-0.006) for {'C': 100.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.500 (+/-0.000) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.697 (+/-0.307) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.697 (+/-0.308) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.694 (+/-0.306) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.641 (+/-0.244) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.570 (+/-0.227) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.493 (+/-0.012) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.497 (+/-0.005) for {'C': 1000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.500 (+/-0.000) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.697 (+/-0.307) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.696 (+/-0.308) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.694 (+/-0.305) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.662 (+/-0.226) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.551 (+/-0.187) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.560 (+/-0.225) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.495 (+/-0.008) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.497 (+/-0.005) for {'C': 10000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.500 (+/-0.000) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.697 (+/-0.307) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.696 (+/-0.308) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.693 (+/-0.304) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.685 (+/-0.299) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.640 (+/-0.243) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.561 (+/-0.200) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.566 (+/-0.229) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.495 (+/-0.008) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.497 (+/-0.005) for {'C': 100000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.760 (+/-0.153) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.696 (+/-0.306) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.690 (+/-0.296) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.682 (+/-0.282) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.682 (+/-0.281) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.550 (+/-0.183) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.575 (+/-0.224) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.543 (+/-0.200) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.495 (+/-0.008) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.497 (+/-0.005) for {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.657 (+/-0.216) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.574 (+/-0.167) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.692 (+/-0.291) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.681 (+/-0.279) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.679 (+/-0.276) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.677 (+/-0.284) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.508 (+/-0.155) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.547 (+/-0.226) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.543 (+/-0.200) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.495 (+/-0.008) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.497 (+/-0.005) for {'C': 10000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.681 (+/-0.293) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-08}
0.525 (+/-0.114) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
0.688 (+/-0.284) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-06}
0.679 (+/-0.275) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1e-05}
0.679 (+/-0.277) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.0001}
0.675 (+/-0.289) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.001}
0.506 (+/-0.162) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.01}
0.548 (+/-0.224) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 0.1}
0.543 (+/-0.200) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 1.0}
0.495 (+/-0.008) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 10.0}
0.497 (+/-0.005) for {'C': 100000000.0, 'kernel': 'rbf', 'gamma': 100.0}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.657 (+/-0.216) for {'C': 0.1, 'kernel': 'linear'}
0.697 (+/-0.308) for {'C': 1.0, 'kernel': 'linear'}
0.696 (+/-0.306) for {'C': 10.0, 'kernel': 'linear'}
0.688 (+/-0.301) for {'C': 100.0, 'kernel': 'linear'}
0.685 (+/-0.298) for {'C': 1000.0, 'kernel': 'linear'}
0.685 (+/-0.298) for {'C': 10000.0, 'kernel': 'linear'}
0.684 (+/-0.298) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 1000000.0, 'kernel': 'rbf', 'gamma': 1e-07}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.7602533184928684
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.496 (+/-0.001) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.496 (+/-0.001) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.496 (+/-0.001) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.496 (+/-0.001) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.496 (+/-0.001) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.496 (+/-0.001) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.496 (+/-0.001) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.496 (+/-0.001) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.547 (+/-0.301) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.697 (+/-0.437) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.639 (+/-0.326) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.496 (+/-0.001) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.496 (+/-0.001) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.496 (+/-0.001) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.496 (+/-0.001) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.496 (+/-0.001) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.496 (+/-0.001) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.496 (+/-0.001) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.547 (+/-0.301) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.689 (+/-0.436) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.639 (+/-0.326) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.660 (+/-0.290) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.496 (+/-0.001) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.496 (+/-0.001) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.496 (+/-0.001) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.496 (+/-0.001) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.496 (+/-0.001) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.496 (+/-0.001) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.597 (+/-0.402) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.689 (+/-0.436) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.639 (+/-0.326) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.643 (+/-0.209) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.618 (+/-0.172) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.496 (+/-0.001) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.496 (+/-0.001) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.496 (+/-0.001) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.496 (+/-0.001) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.496 (+/-0.001) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.597 (+/-0.402) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.689 (+/-0.436) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.639 (+/-0.326) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.627 (+/-0.185) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.625 (+/-0.179) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.630 (+/-0.189) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.496 (+/-0.001) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.496 (+/-0.001) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.496 (+/-0.001) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.496 (+/-0.001) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.747 (+/-0.502) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.702 (+/-0.420) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.689 (+/-0.374) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.677 (+/-0.299) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.639 (+/-0.167) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.660 (+/-0.136) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.630 (+/-0.189) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.656 (+/-0.312) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.666 (+/-0.275) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.685 (+/-0.352) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.635 (+/-0.153) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.644 (+/-0.120) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.669 (+/-0.142) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.630 (+/-0.189) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.547 (+/-0.301) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.604 (+/-0.171) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.672 (+/-0.287) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.630 (+/-0.189) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.714 (+/-0.213) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.614 (+/-0.167) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.686 (+/-0.156) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.621 (+/-0.177) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.496 (+/-0.001) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.496 (+/-0.001) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.647 (+/-0.460) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.747 (+/-0.502) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.544 (+/-0.101) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.648 (+/-0.292) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.600 (+/-0.138) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.712 (+/-0.218) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.635 (+/-0.190) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.686 (+/-0.156) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.626 (+/-0.188) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.496 (+/-0.001) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.647 (+/-0.460) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.747 (+/-0.502) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.702 (+/-0.429) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.511 (+/-0.031) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.623 (+/-0.295) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.624 (+/-0.166) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.720 (+/-0.217) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.631 (+/-0.186) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.677 (+/-0.150) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.624 (+/-0.186) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.647 (+/-0.460) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.722 (+/-0.473) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.739 (+/-0.451) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.691 (+/-0.370) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.503 (+/-0.010) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.594 (+/-0.196) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.619 (+/-0.169) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.713 (+/-0.227) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.659 (+/-0.285) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.702 (+/-0.243) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.636 (+/-0.280) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.722 (+/-0.473) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.714 (+/-0.418) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.689 (+/-0.299) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.732 (+/-0.391) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.453 (+/-0.300) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.589 (+/-0.194) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.609 (+/-0.185) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.707 (+/-0.234) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.659 (+/-0.285) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.702 (+/-0.243) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.635 (+/-0.280) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.630 (+/-0.189) for {'C': 1.0, 'kernel': 'linear'}
0.650 (+/-0.281) for {'C': 10.0, 'kernel': 'linear'}
0.648 (+/-0.282) for {'C': 100.0, 'kernel': 'linear'}
0.619 (+/-0.177) for {'C': 1000.0, 'kernel': 'linear'}
0.598 (+/-0.159) for {'C': 10000.0, 'kernel': 'linear'}
0.585 (+/-0.147) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.500 (+/-0.000) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.500 (+/-0.000) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.500 (+/-0.000) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.500 (+/-0.000) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.500 (+/-0.000) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.500 (+/-0.000) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.500 (+/-0.000) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.500 (+/-0.000) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.525 (+/-0.150) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.616 (+/-0.238) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.615 (+/-0.237) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.500 (+/-0.000) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.500 (+/-0.000) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.500 (+/-0.000) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.500 (+/-0.000) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.500 (+/-0.000) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.500 (+/-0.000) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.500 (+/-0.000) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.525 (+/-0.150) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.616 (+/-0.238) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.615 (+/-0.237) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.656 (+/-0.215) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.500 (+/-0.000) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.500 (+/-0.000) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.500 (+/-0.000) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.500 (+/-0.000) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.500 (+/-0.000) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.500 (+/-0.000) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.550 (+/-0.200) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.616 (+/-0.238) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.615 (+/-0.237) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.656 (+/-0.216) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.680 (+/-0.294) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.500 (+/-0.000) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.500 (+/-0.000) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.500 (+/-0.000) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.500 (+/-0.000) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.500 (+/-0.000) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.550 (+/-0.200) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.616 (+/-0.238) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.615 (+/-0.237) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.656 (+/-0.215) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.697 (+/-0.307) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.697 (+/-0.308) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.500 (+/-0.000) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.500 (+/-0.000) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.500 (+/-0.000) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.500 (+/-0.000) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.617 (+/-0.238) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.641 (+/-0.235) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.640 (+/-0.236) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.656 (+/-0.216) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.705 (+/-0.270) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.747 (+/-0.234) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.697 (+/-0.308) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.500 (+/-0.002) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.640 (+/-0.235) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.680 (+/-0.193) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.656 (+/-0.215) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.697 (+/-0.211) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.730 (+/-0.230) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.747 (+/-0.234) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.697 (+/-0.308) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.525 (+/-0.150) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.677 (+/-0.283) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.721 (+/-0.278) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.681 (+/-0.294) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.771 (+/-0.163) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.680 (+/-0.293) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.747 (+/-0.234) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.696 (+/-0.308) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.500 (+/-0.000) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.500 (+/-0.000) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.575 (+/-0.229) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.616 (+/-0.239) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.658 (+/-0.229) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.719 (+/-0.276) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.679 (+/-0.293) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.772 (+/-0.166) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.697 (+/-0.307) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.747 (+/-0.234) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.697 (+/-0.307) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.500 (+/-0.000) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.575 (+/-0.229) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.616 (+/-0.239) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.659 (+/-0.215) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.597 (+/-0.244) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.694 (+/-0.303) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.720 (+/-0.274) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.772 (+/-0.166) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.681 (+/-0.294) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.722 (+/-0.166) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.697 (+/-0.306) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.575 (+/-0.229) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.616 (+/-0.239) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.641 (+/-0.237) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.685 (+/-0.198) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.571 (+/-0.210) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.678 (+/-0.251) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.720 (+/-0.272) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.771 (+/-0.166) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.681 (+/-0.295) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.747 (+/-0.234) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.696 (+/-0.305) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.616 (+/-0.239) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.640 (+/-0.237) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.657 (+/-0.216) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.666 (+/-0.193) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.539 (+/-0.178) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.662 (+/-0.228) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.694 (+/-0.301) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.771 (+/-0.161) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.681 (+/-0.295) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.747 (+/-0.234) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.696 (+/-0.303) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.616 (+/-0.239) for {'C': 0.1, 'kernel': 'linear'}
0.697 (+/-0.308) for {'C': 1.0, 'kernel': 'linear'}
0.697 (+/-0.308) for {'C': 10.0, 'kernel': 'linear'}
0.697 (+/-0.307) for {'C': 100.0, 'kernel': 'linear'}
0.696 (+/-0.307) for {'C': 1000.0, 'kernel': 'linear'}
0.695 (+/-0.307) for {'C': 10000.0, 'kernel': 'linear'}
0.694 (+/-0.307) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.7717933259131009
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.496 (+/-0.001) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.496 (+/-0.001) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.496 (+/-0.001) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.496 (+/-0.001) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.496 (+/-0.001) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.496 (+/-0.001) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.597 (+/-0.402) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.697 (+/-0.437) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.639 (+/-0.326) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.627 (+/-0.185) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.625 (+/-0.179) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.496 (+/-0.001) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.496 (+/-0.001) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.496 (+/-0.001) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.496 (+/-0.001) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.496 (+/-0.001) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.647 (+/-0.460) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.664 (+/-0.388) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.639 (+/-0.326) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.627 (+/-0.185) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.618 (+/-0.172) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.652 (+/-0.123) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.496 (+/-0.001) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.496 (+/-0.001) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.496 (+/-0.001) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.496 (+/-0.001) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.496 (+/-0.001) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.668 (+/-0.295) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.643 (+/-0.306) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.660 (+/-0.290) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.637 (+/-0.122) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.647 (+/-0.124) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.660 (+/-0.136) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.496 (+/-0.001) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.496 (+/-0.001) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.496 (+/-0.001) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.496 (+/-0.001) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.764 (+/-0.421) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.600 (+/-0.167) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.627 (+/-0.291) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.620 (+/-0.172) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.706 (+/-0.214) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.715 (+/-0.210) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.630 (+/-0.189) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.496 (+/-0.001) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.496 (+/-0.001) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.496 (+/-0.001) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.496 (+/-0.001) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.631 (+/-0.196) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.564 (+/-0.085) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.603 (+/-0.155) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.699 (+/-0.211) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.686 (+/-0.248) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.715 (+/-0.210) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.621 (+/-0.177) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.496 (+/-0.001) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.747 (+/-0.502) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.548 (+/-0.097) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.600 (+/-0.112) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.594 (+/-0.157) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.697 (+/-0.215) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.623 (+/-0.184) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.673 (+/-0.153) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.612 (+/-0.164) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.496 (+/-0.001) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.747 (+/-0.502) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.697 (+/-0.437) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.527 (+/-0.071) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.613 (+/-0.206) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.581 (+/-0.166) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.698 (+/-0.224) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.630 (+/-0.189) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.668 (+/-0.146) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.609 (+/-0.179) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.496 (+/-0.001) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.747 (+/-0.502) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.697 (+/-0.437) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.664 (+/-0.388) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.520 (+/-0.060) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.590 (+/-0.178) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.567 (+/-0.126) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.684 (+/-0.232) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.630 (+/-0.189) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.656 (+/-0.138) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.594 (+/-0.159) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.747 (+/-0.502) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.697 (+/-0.437) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.714 (+/-0.352) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.667 (+/-0.311) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.560 (+/-0.294) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.556 (+/-0.149) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.575 (+/-0.159) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.639 (+/-0.136) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.626 (+/-0.176) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.656 (+/-0.138) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.587 (+/-0.154) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.697 (+/-0.437) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.689 (+/-0.299) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.681 (+/-0.297) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.725 (+/-0.321) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.502 (+/-0.445) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.534 (+/-0.146) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.573 (+/-0.170) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.634 (+/-0.142) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.626 (+/-0.176) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.671 (+/-0.249) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.604 (+/-0.280) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.689 (+/-0.299) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.673 (+/-0.293) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.673 (+/-0.293) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.720 (+/-0.326) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.499 (+/-0.446) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.529 (+/-0.146) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.567 (+/-0.156) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.632 (+/-0.145) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.626 (+/-0.176) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.667 (+/-0.253) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.597 (+/-0.281) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.727 (+/-0.397) for {'C': 0.1, 'kernel': 'linear'}
0.625 (+/-0.179) for {'C': 1.0, 'kernel': 'linear'}
0.632 (+/-0.282) for {'C': 10.0, 'kernel': 'linear'}
0.597 (+/-0.290) for {'C': 100.0, 'kernel': 'linear'}
0.562 (+/-0.158) for {'C': 1000.0, 'kernel': 'linear'}
0.558 (+/-0.134) for {'C': 10000.0, 'kernel': 'linear'}
0.544 (+/-0.100) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.500 (+/-0.000) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.500 (+/-0.000) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.500 (+/-0.000) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.500 (+/-0.000) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.500 (+/-0.000) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.500 (+/-0.000) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.550 (+/-0.200) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.616 (+/-0.238) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.615 (+/-0.237) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.656 (+/-0.215) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.697 (+/-0.307) for {'C': 100000.00000000003, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.500 (+/-0.000) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.500 (+/-0.000) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.500 (+/-0.000) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.500 (+/-0.000) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.500 (+/-0.000) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.575 (+/-0.230) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.616 (+/-0.238) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.615 (+/-0.237) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.656 (+/-0.215) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.672 (+/-0.239) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.746 (+/-0.233) for {'C': 158489.31924611147, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.500 (+/-0.000) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.500 (+/-0.000) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.500 (+/-0.000) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.500 (+/-0.000) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.500 (+/-0.000) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.665 (+/-0.221) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.640 (+/-0.235) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.656 (+/-0.215) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.705 (+/-0.151) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.722 (+/-0.165) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.747 (+/-0.233) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.500 (+/-0.000) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.500 (+/-0.000) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.500 (+/-0.000) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.500 (+/-0.002) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.657 (+/-0.217) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.678 (+/-0.190) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.679 (+/-0.291) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.697 (+/-0.306) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.772 (+/-0.166) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.772 (+/-0.167) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.697 (+/-0.308) for {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.500 (+/-0.000) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.500 (+/-0.000) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.500 (+/-0.000) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.500 (+/-0.002) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.697 (+/-0.305) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.675 (+/-0.188) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.701 (+/-0.255) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.771 (+/-0.164) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.747 (+/-0.233) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.772 (+/-0.167) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.696 (+/-0.308) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.500 (+/-0.000) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.617 (+/-0.238) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.679 (+/-0.275) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.760 (+/-0.153) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.712 (+/-0.251) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.771 (+/-0.164) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.697 (+/-0.306) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.747 (+/-0.234) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.696 (+/-0.306) for {'C': 999999.9999999999, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.500 (+/-0.000) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.617 (+/-0.238) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.616 (+/-0.237) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.612 (+/-0.269) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.725 (+/-0.243) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.677 (+/-0.252) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.771 (+/-0.163) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.697 (+/-0.308) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.747 (+/-0.234) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.696 (+/-0.303) for {'C': 1584893.192461114, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.500 (+/-0.000) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.617 (+/-0.238) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.616 (+/-0.238) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.616 (+/-0.238) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.581 (+/-0.185) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.682 (+/-0.302) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.659 (+/-0.234) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.770 (+/-0.160) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.697 (+/-0.308) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.747 (+/-0.234) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.695 (+/-0.300) for {'C': 2511886.431509582, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.617 (+/-0.238) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.616 (+/-0.238) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.657 (+/-0.217) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.670 (+/-0.183) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.572 (+/-0.193) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.669 (+/-0.307) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.649 (+/-0.244) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.768 (+/-0.160) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.697 (+/-0.307) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.747 (+/-0.234) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.694 (+/-0.299) for {'C': 3981071.705534977, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.616 (+/-0.238) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.657 (+/-0.216) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.657 (+/-0.216) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.726 (+/-0.074) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.545 (+/-0.214) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.608 (+/-0.222) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.645 (+/-0.249) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.768 (+/-0.161) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.697 (+/-0.307) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.746 (+/-0.233) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.693 (+/-0.295) for {'C': 6309573.444801927, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.657 (+/-0.216) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000008e-08}
0.656 (+/-0.216) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.5848931924611156e-08}
0.656 (+/-0.216) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095844e-08}
0.697 (+/-0.143) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.981071705534971e-08}
0.514 (+/-0.274) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801934e-08}
0.574 (+/-0.167) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000005e-07}
0.644 (+/-0.248) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.584893192461115e-07}
0.767 (+/-0.159) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 2.5118864315095833e-07}
0.697 (+/-0.307) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.746 (+/-0.233) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.691 (+/-0.290) for {'C': 9999999.999999994, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.657 (+/-0.216) for {'C': 0.1, 'kernel': 'linear'}
0.697 (+/-0.308) for {'C': 1.0, 'kernel': 'linear'}
0.696 (+/-0.306) for {'C': 10.0, 'kernel': 'linear'}
0.688 (+/-0.301) for {'C': 100.0, 'kernel': 'linear'}
0.685 (+/-0.298) for {'C': 1000.0, 'kernel': 'linear'}
0.685 (+/-0.298) for {'C': 10000.0, 'kernel': 'linear'}
0.684 (+/-0.298) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 398107.1705534969, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.7719535823233572
循环迭代之前,delta is: [6.01892829e+05 5.30957344e-07]
执行tunemodel()函数前,使用的grid search parameter是:
[{'C': array([251188.64315096, 275422.87033382, 301995.1720402 , 331131.12148259,
363078.0547701 , 398107.1705535 , 436515.83224017, 478630.09232264,
524807.46024977, 575439.93733716, 630957.34448019]), 'kernel': ['rbf'], 'gamma': array([3.98107171e-07, 4.36515832e-07, 4.78630092e-07, 5.24807460e-07,
5.75439937e-07, 6.30957344e-07, 6.91830971e-07, 7.58577575e-07,
8.31763771e-07, 9.12010839e-07, 1.00000000e-06])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}]
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.639 (+/-0.326) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.739 (+/-0.391) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.727 (+/-0.397) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.702 (+/-0.355) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.702 (+/-0.355) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.643 (+/-0.209) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.618 (+/-0.169) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.614 (+/-0.166) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.623 (+/-0.184) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.618 (+/-0.172) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.618 (+/-0.172) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.681 (+/-0.372) for {'C': 275422.87033381715, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.731 (+/-0.393) for {'C': 275422.87033381715, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.702 (+/-0.355) for {'C': 275422.87033381715, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.660 (+/-0.290) for {'C': 275422.87033381715, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.627 (+/-0.185) for {'C': 275422.87033381715, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.635 (+/-0.198) for {'C': 275422.87033381715, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.614 (+/-0.166) for {'C': 275422.87033381715, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.618 (+/-0.172) for {'C': 275422.87033381715, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.618 (+/-0.172) for {'C': 275422.87033381715, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.618 (+/-0.172) for {'C': 275422.87033381715, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.614 (+/-0.167) for {'C': 275422.87033381715, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.727 (+/-0.397) for {'C': 301995.17204020155, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.702 (+/-0.355) for {'C': 301995.17204020155, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.668 (+/-0.295) for {'C': 301995.17204020155, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.635 (+/-0.198) for {'C': 301995.17204020155, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.618 (+/-0.169) for {'C': 301995.17204020155, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.623 (+/-0.183) for {'C': 301995.17204020155, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.618 (+/-0.172) for {'C': 301995.17204020155, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.625 (+/-0.179) for {'C': 301995.17204020155, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.618 (+/-0.172) for {'C': 301995.17204020155, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.618 (+/-0.172) for {'C': 301995.17204020155, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.622 (+/-0.174) for {'C': 301995.17204020155, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.693 (+/-0.354) for {'C': 331131.12148259126, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.693 (+/-0.354) for {'C': 331131.12148259126, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.627 (+/-0.185) for {'C': 331131.12148259126, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.627 (+/-0.185) for {'C': 331131.12148259126, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.614 (+/-0.166) for {'C': 331131.12148259126, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.618 (+/-0.172) for {'C': 331131.12148259126, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.618 (+/-0.172) for {'C': 331131.12148259126, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.622 (+/-0.174) for {'C': 331131.12148259126, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.618 (+/-0.172) for {'C': 331131.12148259126, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.614 (+/-0.167) for {'C': 331131.12148259126, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.622 (+/-0.174) for {'C': 331131.12148259126, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.652 (+/-0.284) for {'C': 363078.0547701017, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.627 (+/-0.185) for {'C': 363078.0547701017, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.618 (+/-0.169) for {'C': 363078.0547701017, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.623 (+/-0.183) for {'C': 363078.0547701017, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.610 (+/-0.163) for {'C': 363078.0547701017, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.625 (+/-0.179) for {'C': 363078.0547701017, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.618 (+/-0.172) for {'C': 363078.0547701017, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.622 (+/-0.174) for {'C': 363078.0547701017, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.625 (+/-0.179) for {'C': 363078.0547701017, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.627 (+/-0.147) for {'C': 363078.0547701017, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.622 (+/-0.174) for {'C': 363078.0547701017, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.627 (+/-0.185) for {'C': 398107.17055349785, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.618 (+/-0.169) for {'C': 398107.17055349785, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.614 (+/-0.166) for {'C': 398107.17055349785, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.618 (+/-0.172) for {'C': 398107.17055349785, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.618 (+/-0.172) for {'C': 398107.17055349785, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.625 (+/-0.179) for {'C': 398107.17055349785, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.630 (+/-0.152) for {'C': 398107.17055349785, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.622 (+/-0.174) for {'C': 398107.17055349785, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.625 (+/-0.179) for {'C': 398107.17055349785, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.627 (+/-0.147) for {'C': 398107.17055349785, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.630 (+/-0.189) for {'C': 398107.17055349785, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.618 (+/-0.169) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.610 (+/-0.163) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.610 (+/-0.163) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.625 (+/-0.179) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.625 (+/-0.179) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.625 (+/-0.179) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.630 (+/-0.152) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.622 (+/-0.174) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.625 (+/-0.179) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.627 (+/-0.147) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.647 (+/-0.167) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.622 (+/-0.174) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.610 (+/-0.163) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.618 (+/-0.172) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.625 (+/-0.179) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.625 (+/-0.179) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.638 (+/-0.157) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.647 (+/-0.124) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.622 (+/-0.174) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.630 (+/-0.189) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.641 (+/-0.122) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.647 (+/-0.167) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.626 (+/-0.188) for {'C': 524807.460249773, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.625 (+/-0.179) for {'C': 524807.460249773, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.625 (+/-0.179) for {'C': 524807.460249773, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.625 (+/-0.179) for {'C': 524807.460249773, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.630 (+/-0.189) for {'C': 524807.460249773, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.655 (+/-0.126) for {'C': 524807.460249773, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.655 (+/-0.126) for {'C': 524807.460249773, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.622 (+/-0.174) for {'C': 524807.460249773, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.630 (+/-0.189) for {'C': 524807.460249773, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.636 (+/-0.159) for {'C': 524807.460249773, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.630 (+/-0.189) for {'C': 524807.460249773, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.643 (+/-0.168) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.642 (+/-0.158) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.625 (+/-0.179) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.625 (+/-0.179) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.643 (+/-0.167) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.655 (+/-0.126) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.642 (+/-0.158) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.622 (+/-0.174) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.630 (+/-0.189) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.636 (+/-0.169) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.630 (+/-0.189) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.639 (+/-0.167) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.655 (+/-0.126) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.630 (+/-0.189) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.663 (+/-0.138) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.643 (+/-0.167) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.660 (+/-0.136) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.642 (+/-0.158) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.642 (+/-0.158) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.630 (+/-0.189) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.636 (+/-0.169) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.630 (+/-0.189) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.747 (+/-0.502) for {'C': 0.1, 'kernel': 'linear'}
0.630 (+/-0.189) for {'C': 1.0, 'kernel': 'linear'}
0.650 (+/-0.281) for {'C': 10.0, 'kernel': 'linear'}
0.648 (+/-0.282) for {'C': 100.0, 'kernel': 'linear'}
0.619 (+/-0.177) for {'C': 1000.0, 'kernel': 'linear'}
0.598 (+/-0.159) for {'C': 10000.0, 'kernel': 'linear'}
0.585 (+/-0.147) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.615 (+/-0.237) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.657 (+/-0.217) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.657 (+/-0.216) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.657 (+/-0.216) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.657 (+/-0.216) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.656 (+/-0.216) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.656 (+/-0.215) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.655 (+/-0.215) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.680 (+/-0.295) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.680 (+/-0.294) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.680 (+/-0.294) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.640 (+/-0.236) for {'C': 275422.87033381715, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.657 (+/-0.216) for {'C': 275422.87033381715, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.657 (+/-0.216) for {'C': 275422.87033381715, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.656 (+/-0.215) for {'C': 275422.87033381715, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.656 (+/-0.215) for {'C': 275422.87033381715, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.656 (+/-0.215) for {'C': 275422.87033381715, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.655 (+/-0.215) for {'C': 275422.87033381715, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.680 (+/-0.294) for {'C': 275422.87033381715, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.680 (+/-0.294) for {'C': 275422.87033381715, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.680 (+/-0.294) for {'C': 275422.87033381715, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.680 (+/-0.294) for {'C': 275422.87033381715, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.657 (+/-0.216) for {'C': 301995.17204020155, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.657 (+/-0.216) for {'C': 301995.17204020155, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.656 (+/-0.216) for {'C': 301995.17204020155, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.656 (+/-0.215) for {'C': 301995.17204020155, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.656 (+/-0.215) for {'C': 301995.17204020155, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.656 (+/-0.215) for {'C': 301995.17204020155, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.680 (+/-0.294) for {'C': 301995.17204020155, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.697 (+/-0.307) for {'C': 301995.17204020155, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.680 (+/-0.294) for {'C': 301995.17204020155, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.680 (+/-0.295) for {'C': 301995.17204020155, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.697 (+/-0.307) for {'C': 301995.17204020155, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.656 (+/-0.216) for {'C': 331131.12148259126, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.656 (+/-0.216) for {'C': 331131.12148259126, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.656 (+/-0.215) for {'C': 331131.12148259126, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.656 (+/-0.215) for {'C': 331131.12148259126, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.655 (+/-0.215) for {'C': 331131.12148259126, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.680 (+/-0.294) for {'C': 331131.12148259126, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.680 (+/-0.294) for {'C': 331131.12148259126, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.697 (+/-0.306) for {'C': 331131.12148259126, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.680 (+/-0.294) for {'C': 331131.12148259126, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.680 (+/-0.294) for {'C': 331131.12148259126, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.696 (+/-0.307) for {'C': 331131.12148259126, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.656 (+/-0.215) for {'C': 363078.0547701017, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.656 (+/-0.215) for {'C': 363078.0547701017, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.656 (+/-0.215) for {'C': 363078.0547701017, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.656 (+/-0.215) for {'C': 363078.0547701017, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.655 (+/-0.215) for {'C': 363078.0547701017, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.697 (+/-0.307) for {'C': 363078.0547701017, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.680 (+/-0.295) for {'C': 363078.0547701017, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.697 (+/-0.306) for {'C': 363078.0547701017, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.697 (+/-0.307) for {'C': 363078.0547701017, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.705 (+/-0.269) for {'C': 363078.0547701017, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.696 (+/-0.307) for {'C': 363078.0547701017, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.656 (+/-0.215) for {'C': 398107.17055349785, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.656 (+/-0.215) for {'C': 398107.17055349785, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.655 (+/-0.215) for {'C': 398107.17055349785, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.680 (+/-0.294) for {'C': 398107.17055349785, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.680 (+/-0.294) for {'C': 398107.17055349785, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.697 (+/-0.307) for {'C': 398107.17055349785, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.705 (+/-0.269) for {'C': 398107.17055349785, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.697 (+/-0.307) for {'C': 398107.17055349785, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.697 (+/-0.308) for {'C': 398107.17055349785, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.705 (+/-0.269) for {'C': 398107.17055349785, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.697 (+/-0.308) for {'C': 398107.17055349785, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.656 (+/-0.215) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.655 (+/-0.215) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.655 (+/-0.215) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.697 (+/-0.307) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.697 (+/-0.307) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.697 (+/-0.307) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.705 (+/-0.269) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.696 (+/-0.307) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.697 (+/-0.308) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.705 (+/-0.269) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.722 (+/-0.278) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.680 (+/-0.294) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.655 (+/-0.215) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.680 (+/-0.294) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.697 (+/-0.307) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.697 (+/-0.307) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.722 (+/-0.277) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.730 (+/-0.231) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.696 (+/-0.307) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.697 (+/-0.308) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.730 (+/-0.230) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.722 (+/-0.278) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.680 (+/-0.294) for {'C': 524807.460249773, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.697 (+/-0.307) for {'C': 524807.460249773, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.697 (+/-0.307) for {'C': 524807.460249773, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.697 (+/-0.307) for {'C': 524807.460249773, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.697 (+/-0.308) for {'C': 524807.460249773, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.747 (+/-0.233) for {'C': 524807.460249773, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.747 (+/-0.233) for {'C': 524807.460249773, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.696 (+/-0.307) for {'C': 524807.460249773, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.697 (+/-0.308) for {'C': 524807.460249773, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.721 (+/-0.277) for {'C': 524807.460249773, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.697 (+/-0.308) for {'C': 524807.460249773, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.705 (+/-0.269) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.722 (+/-0.277) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.697 (+/-0.307) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.697 (+/-0.308) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.722 (+/-0.277) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.747 (+/-0.233) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.722 (+/-0.278) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.696 (+/-0.307) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.697 (+/-0.308) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.721 (+/-0.277) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.697 (+/-0.308) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.705 (+/-0.270) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.747 (+/-0.233) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.697 (+/-0.307) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.747 (+/-0.233) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.722 (+/-0.277) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.747 (+/-0.234) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.722 (+/-0.278) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.722 (+/-0.278) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.697 (+/-0.308) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.721 (+/-0.277) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.697 (+/-0.308) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.616 (+/-0.239) for {'C': 0.1, 'kernel': 'linear'}
0.697 (+/-0.308) for {'C': 1.0, 'kernel': 'linear'}
0.697 (+/-0.308) for {'C': 10.0, 'kernel': 'linear'}
0.697 (+/-0.307) for {'C': 100.0, 'kernel': 'linear'}
0.696 (+/-0.307) for {'C': 1000.0, 'kernel': 'linear'}
0.695 (+/-0.307) for {'C': 10000.0, 'kernel': 'linear'}
0.694 (+/-0.307) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.7467928106191771
针对precision这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.637 (+/-0.122) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.639 (+/-0.154) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.627 (+/-0.147) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.655 (+/-0.126) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.647 (+/-0.124) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.647 (+/-0.124) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.655 (+/-0.126) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.649 (+/-0.126) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.710 (+/-0.209) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.656 (+/-0.138) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.660 (+/-0.136) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.635 (+/-0.118) for {'C': 275422.87033381715, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.652 (+/-0.123) for {'C': 275422.87033381715, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.647 (+/-0.124) for {'C': 275422.87033381715, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.652 (+/-0.123) for {'C': 275422.87033381715, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.697 (+/-0.213) for {'C': 275422.87033381715, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.647 (+/-0.124) for {'C': 275422.87033381715, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.655 (+/-0.126) for {'C': 275422.87033381715, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.649 (+/-0.126) for {'C': 275422.87033381715, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.660 (+/-0.136) for {'C': 275422.87033381715, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.656 (+/-0.138) for {'C': 275422.87033381715, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.647 (+/-0.167) for {'C': 275422.87033381715, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.686 (+/-0.221) for {'C': 301995.17204020155, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.652 (+/-0.123) for {'C': 301995.17204020155, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.645 (+/-0.127) for {'C': 301995.17204020155, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.652 (+/-0.123) for {'C': 301995.17204020155, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.710 (+/-0.209) for {'C': 301995.17204020155, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.705 (+/-0.207) for {'C': 301995.17204020155, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.652 (+/-0.123) for {'C': 301995.17204020155, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.649 (+/-0.126) for {'C': 301995.17204020155, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.660 (+/-0.136) for {'C': 301995.17204020155, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.656 (+/-0.138) for {'C': 301995.17204020155, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.647 (+/-0.167) for {'C': 301995.17204020155, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.709 (+/-0.212) for {'C': 331131.12148259126, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.647 (+/-0.124) for {'C': 331131.12148259126, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.645 (+/-0.127) for {'C': 331131.12148259126, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.639 (+/-0.154) for {'C': 331131.12148259126, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.710 (+/-0.209) for {'C': 331131.12148259126, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.710 (+/-0.209) for {'C': 331131.12148259126, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.655 (+/-0.126) for {'C': 331131.12148259126, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.652 (+/-0.129) for {'C': 331131.12148259126, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.660 (+/-0.136) for {'C': 331131.12148259126, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.643 (+/-0.167) for {'C': 331131.12148259126, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.647 (+/-0.167) for {'C': 331131.12148259126, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.709 (+/-0.212) for {'C': 363078.0547701017, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.647 (+/-0.124) for {'C': 363078.0547701017, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.641 (+/-0.122) for {'C': 363078.0547701017, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.647 (+/-0.167) for {'C': 363078.0547701017, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.710 (+/-0.209) for {'C': 363078.0547701017, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.710 (+/-0.209) for {'C': 363078.0547701017, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.655 (+/-0.126) for {'C': 363078.0547701017, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.652 (+/-0.129) for {'C': 363078.0547701017, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.660 (+/-0.136) for {'C': 363078.0547701017, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.643 (+/-0.167) for {'C': 363078.0547701017, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.630 (+/-0.189) for {'C': 363078.0547701017, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.706 (+/-0.214) for {'C': 398107.17055349785, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.698 (+/-0.213) for {'C': 398107.17055349785, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.654 (+/-0.137) for {'C': 398107.17055349785, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.660 (+/-0.177) for {'C': 398107.17055349785, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.710 (+/-0.209) for {'C': 398107.17055349785, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.715 (+/-0.210) for {'C': 398107.17055349785, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.655 (+/-0.126) for {'C': 398107.17055349785, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.652 (+/-0.129) for {'C': 398107.17055349785, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.630 (+/-0.189) for {'C': 398107.17055349785, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.643 (+/-0.167) for {'C': 398107.17055349785, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.630 (+/-0.189) for {'C': 398107.17055349785, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.706 (+/-0.214) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.698 (+/-0.213) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.646 (+/-0.122) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.643 (+/-0.202) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.715 (+/-0.210) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.715 (+/-0.210) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.643 (+/-0.167) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.649 (+/-0.126) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.630 (+/-0.189) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.643 (+/-0.167) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.630 (+/-0.189) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.689 (+/-0.248) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.698 (+/-0.213) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.658 (+/-0.136) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.651 (+/-0.211) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.715 (+/-0.210) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.715 (+/-0.210) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.630 (+/-0.189) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.644 (+/-0.164) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.630 (+/-0.189) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.643 (+/-0.167) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.625 (+/-0.179) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.689 (+/-0.248) for {'C': 524807.460249773, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.701 (+/-0.212) for {'C': 524807.460249773, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.658 (+/-0.136) for {'C': 524807.460249773, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.651 (+/-0.211) for {'C': 524807.460249773, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.715 (+/-0.210) for {'C': 524807.460249773, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.715 (+/-0.210) for {'C': 524807.460249773, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.635 (+/-0.198) for {'C': 524807.460249773, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.640 (+/-0.164) for {'C': 524807.460249773, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.630 (+/-0.189) for {'C': 524807.460249773, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.626 (+/-0.188) for {'C': 524807.460249773, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.625 (+/-0.179) for {'C': 524807.460249773, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.686 (+/-0.248) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.701 (+/-0.212) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.658 (+/-0.136) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.651 (+/-0.211) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.715 (+/-0.210) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.715 (+/-0.210) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.639 (+/-0.202) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.640 (+/-0.164) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.630 (+/-0.189) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.626 (+/-0.188) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.625 (+/-0.179) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.686 (+/-0.248) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.697 (+/-0.216) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.661 (+/-0.132) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.651 (+/-0.211) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.715 (+/-0.210) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.715 (+/-0.210) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.639 (+/-0.202) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.632 (+/-0.154) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.627 (+/-0.185) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.636 (+/-0.203) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.621 (+/-0.177) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.496 (+/-0.001) for {'C': 1e-05, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.0001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.001, 'kernel': 'linear'}
0.496 (+/-0.001) for {'C': 0.01, 'kernel': 'linear'}
0.727 (+/-0.397) for {'C': 0.1, 'kernel': 'linear'}
0.625 (+/-0.179) for {'C': 1.0, 'kernel': 'linear'}
0.632 (+/-0.282) for {'C': 10.0, 'kernel': 'linear'}
0.597 (+/-0.290) for {'C': 100.0, 'kernel': 'linear'}
0.562 (+/-0.158) for {'C': 1000.0, 'kernel': 'linear'}
0.558 (+/-0.134) for {'C': 10000.0, 'kernel': 'linear'}
0.544 (+/-0.100) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 1.00 0.99 623
avg / total 0.98 0.99 0.98 629
针对recall这个指标进行参数grid search。
在交叉验证集下Grid计算各参数下的测试集的Score值:
0.705 (+/-0.151) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.722 (+/-0.277) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.705 (+/-0.269) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.747 (+/-0.233) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.730 (+/-0.231) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.722 (+/-0.165) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.747 (+/-0.233) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.746 (+/-0.233) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.772 (+/-0.166) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.747 (+/-0.233) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.747 (+/-0.233) for {'C': 251188.6431509583, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.705 (+/-0.151) for {'C': 275422.87033381715, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.746 (+/-0.233) for {'C': 275422.87033381715, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.730 (+/-0.231) for {'C': 275422.87033381715, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.746 (+/-0.233) for {'C': 275422.87033381715, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.755 (+/-0.173) for {'C': 275422.87033381715, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.722 (+/-0.165) for {'C': 275422.87033381715, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.747 (+/-0.233) for {'C': 275422.87033381715, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.746 (+/-0.233) for {'C': 275422.87033381715, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.747 (+/-0.233) for {'C': 275422.87033381715, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.747 (+/-0.233) for {'C': 275422.87033381715, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.722 (+/-0.278) for {'C': 275422.87033381715, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.730 (+/-0.066) for {'C': 301995.17204020155, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.746 (+/-0.233) for {'C': 301995.17204020155, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.730 (+/-0.231) for {'C': 301995.17204020155, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.746 (+/-0.233) for {'C': 301995.17204020155, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.772 (+/-0.166) for {'C': 301995.17204020155, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.772 (+/-0.166) for {'C': 301995.17204020155, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.746 (+/-0.233) for {'C': 301995.17204020155, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.746 (+/-0.233) for {'C': 301995.17204020155, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.747 (+/-0.233) for {'C': 301995.17204020155, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.747 (+/-0.233) for {'C': 301995.17204020155, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.722 (+/-0.278) for {'C': 301995.17204020155, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.772 (+/-0.166) for {'C': 331131.12148259126, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.746 (+/-0.233) for {'C': 331131.12148259126, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.730 (+/-0.231) for {'C': 331131.12148259126, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.721 (+/-0.277) for {'C': 331131.12148259126, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.772 (+/-0.166) for {'C': 331131.12148259126, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.772 (+/-0.167) for {'C': 331131.12148259126, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.747 (+/-0.233) for {'C': 331131.12148259126, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.746 (+/-0.233) for {'C': 331131.12148259126, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.747 (+/-0.233) for {'C': 331131.12148259126, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.722 (+/-0.278) for {'C': 331131.12148259126, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.722 (+/-0.278) for {'C': 331131.12148259126, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.772 (+/-0.166) for {'C': 363078.0547701017, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.746 (+/-0.233) for {'C': 363078.0547701017, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.730 (+/-0.230) for {'C': 363078.0547701017, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.722 (+/-0.277) for {'C': 363078.0547701017, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.772 (+/-0.166) for {'C': 363078.0547701017, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.772 (+/-0.167) for {'C': 363078.0547701017, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.747 (+/-0.233) for {'C': 363078.0547701017, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.746 (+/-0.233) for {'C': 363078.0547701017, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.747 (+/-0.233) for {'C': 363078.0547701017, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.721 (+/-0.279) for {'C': 363078.0547701017, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.697 (+/-0.308) for {'C': 363078.0547701017, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.772 (+/-0.166) for {'C': 398107.17055349785, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.771 (+/-0.165) for {'C': 398107.17055349785, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.746 (+/-0.233) for {'C': 398107.17055349785, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.722 (+/-0.277) for {'C': 398107.17055349785, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.772 (+/-0.166) for {'C': 398107.17055349785, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.772 (+/-0.167) for {'C': 398107.17055349785, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.747 (+/-0.233) for {'C': 398107.17055349785, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.746 (+/-0.233) for {'C': 398107.17055349785, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.697 (+/-0.308) for {'C': 398107.17055349785, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.721 (+/-0.279) for {'C': 398107.17055349785, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.697 (+/-0.308) for {'C': 398107.17055349785, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.772 (+/-0.166) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.771 (+/-0.165) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.730 (+/-0.230) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.697 (+/-0.307) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.772 (+/-0.167) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.772 (+/-0.167) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.722 (+/-0.277) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.746 (+/-0.233) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.697 (+/-0.308) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.721 (+/-0.279) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.697 (+/-0.308) for {'C': 436515.83224016585, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.747 (+/-0.235) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.771 (+/-0.165) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.747 (+/-0.233) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.697 (+/-0.308) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.772 (+/-0.167) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.772 (+/-0.167) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.697 (+/-0.308) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.722 (+/-0.277) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.697 (+/-0.308) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.721 (+/-0.279) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.697 (+/-0.308) for {'C': 478630.0923226385, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.747 (+/-0.235) for {'C': 524807.460249773, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.771 (+/-0.166) for {'C': 524807.460249773, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.747 (+/-0.233) for {'C': 524807.460249773, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.697 (+/-0.308) for {'C': 524807.460249773, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.772 (+/-0.167) for {'C': 524807.460249773, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.772 (+/-0.167) for {'C': 524807.460249773, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.697 (+/-0.307) for {'C': 524807.460249773, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.721 (+/-0.276) for {'C': 524807.460249773, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.697 (+/-0.308) for {'C': 524807.460249773, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.696 (+/-0.308) for {'C': 524807.460249773, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.697 (+/-0.308) for {'C': 524807.460249773, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.746 (+/-0.234) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.771 (+/-0.166) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.747 (+/-0.233) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.697 (+/-0.308) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.772 (+/-0.167) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.772 (+/-0.167) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.697 (+/-0.308) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.721 (+/-0.276) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.697 (+/-0.308) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.696 (+/-0.309) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.697 (+/-0.308) for {'C': 575439.9373371563, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.747 (+/-0.233) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 3.9810717055349793e-07}
0.771 (+/-0.166) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 4.36515832240167e-07}
0.747 (+/-0.233) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 4.786300923226385e-07}
0.697 (+/-0.308) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 5.248074602497731e-07}
0.772 (+/-0.167) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 5.754399373371578e-07}
0.772 (+/-0.167) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
0.697 (+/-0.308) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 6.918309709189366e-07}
0.721 (+/-0.275) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 7.585775750291844e-07}
0.697 (+/-0.307) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 8.317637711026721e-07}
0.696 (+/-0.309) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 9.120108393559093e-07}
0.696 (+/-0.308) for {'C': 630957.344480193, 'kernel': 'rbf', 'gamma': 1.0000000000000002e-06}
0.500 (+/-0.000) for {'C': 1e-05, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.0001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.001, 'kernel': 'linear'}
0.500 (+/-0.000) for {'C': 0.01, 'kernel': 'linear'}
0.657 (+/-0.216) for {'C': 0.1, 'kernel': 'linear'}
0.697 (+/-0.308) for {'C': 1.0, 'kernel': 'linear'}
0.696 (+/-0.306) for {'C': 10.0, 'kernel': 'linear'}
0.688 (+/-0.301) for {'C': 100.0, 'kernel': 'linear'}
0.685 (+/-0.298) for {'C': 1000.0, 'kernel': 'linear'}
0.685 (+/-0.298) for {'C': 10000.0, 'kernel': 'linear'}
0.684 (+/-0.298) for {'C': 100000.0, 'kernel': 'linear'}
CV交叉验证,分类结果如下报告:
交叉验证集真实标签有: [-1 1]
交叉验证集预测标签有: [-1 1]
检查交叉验证集中测试集标签是否有预测不到的值: set()
得到的分类结果评估报告如下:
precision recall f1-score support
-1 0.00 0.00 0.00 6
1 0.99 0.99 0.99 623
avg / total 0.98 0.98 0.98 629
本轮grid search结果,得到最好的参数选择是: {'C': 398107.17055349785, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}
本轮grid search结果,最好的参数对应的真正测试集上的score是: 0.7719535823233572
第0轮loop中,tunemodel函数得到的最优参数是: ([{'C': array([363078.0547701 , 369828.17978027, 376703.79898391, 383707.24549228,
390840.8957924 , 398107.1705535 , 405508.53544838, 413047.50199016,
420726.62838444, 428548.52039744, 436515.83224017]), 'kernel': ['rbf'], 'gamma': array([5.75439937e-07, 5.86138165e-07, 5.97035287e-07, 6.08135001e-07,
6.19441075e-07, 6.30957344e-07, 6.42687717e-07, 6.54636174e-07,
6.66806769e-07, 6.79203633e-07, 6.91830971e-07])}, {'C': array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,
1.e+03, 1.e+04, 1.e+05]), 'kernel': ['linear']}], {'C': 398107.17055349785, 'kernel': 'rbf', 'gamma': 6.309573444801931e-07}, 0.7719535823233572)
这是第0次迭代微调C和gamma。
第0次迭代,得到delta: [9.31322575e-10 0.00000000e+00]
SVC模型clf_op_iter的参数是: {'kernel': 'rbf', 'decision_function_shape': 'ovr', 'class_weight': {-1: 15}, 'tol': 0.001, 'gamma': 6.309573444801931e-07, 'random_state': 0, 'cache_size': 200, 'verbose': False, 'degree': 3, 'C': 398107.17055349785, 'probability': False, 'shrinking': True, 'coef0': 0.0, 'max_iter': -1}
训练模型SVC对训练样本的预测准确率: 0.9128277817150957
测试集中,预测为舞弊样本的有: (array([ 112, 1230, 1247, 1248], dtype=int64),)
测试集中,实际为舞弊样本的有: (array([1246, 1247, 1248, 1249, 1250, 1251, 1252, 1253, 1254, 1255, 1256],
dtype=int64),)
预测的舞弊样本数目: 4
训练模型SVC对测试样本的预测准确率: 0.9029057406094968
以上是第47次特征筛选。
第47次特征筛选,AUC值是: 0.5901065226907923
X_train_iter_svc.shape is: (1257, 5)
X_test_iter_svc.shape is: (1257, 5)
AUC值随特征数目变化: [0.8173792499635195, 0.8173792499635195, 0.8173792499635195, 0.6814168977090325, 0.6814168977090325, 0.8173792499635195, 0.8173792499635195, 0.8169779658543703, 0.8169779658543703, 0.8169779658543703, 0.726871443163578, 0.726871443163578, 0.726871443163578, 0.726871443163578, 0.726871443163578, 0.726871443163578, 0.8177805340726688, 0.726871443163578, 0.6814168977090325, 0.6814168977090325, 0.6814168977090325, 0.6814168977090325, 0.6818181818181819, 0.6818181818181819, 0.6818181818181819, 0.6818181818181819, 0.7264701590544287, 0.5909090909090908, 0.5454545454545454, 0.7264701590544287, 0.6997300452356633, 0.6467605428279586, 0.8153728294177732, 0.6997300452356633, 0.8177805340726688, 0.5454545454545454, 0.5454545454545454, 0.5454545454545454, 0.5067123887348606, 0.5352035604844594, 0.5267765941923246, 0.4970815701152779, 0.5829563694732233, 0.6616080548664818, 0.5905078067999416, 0.8895374288632715, 0.6802130453815847, 0.5901065226907923]
样本特征数目: [52, 51, 50, 49, 48, 47, 46, 45, 44, 43, 42, 41, 40, 39, 38, 37, 36, 35, 34, 33, 32, 31, 30, 29, 28, 27, 26, 25, 24, 23, 22, 21, 20, 19, 18, 17, 16, 15, 14, 13, 12, 11, 10, 9, 8, 7, 6, 5]